Ultrasensitive reporter protein detection in genetically engineered bacteria

We demonstrate the use of laser-induced fluorescence confocal spectroscopy to measure analyte-stimulated enhanced green fluorescent protein (egfp) synthesis by genetically modified Escherichia coli bioreporter cells. Induction is measured in cell lysates and, since the spectroscopic focal volume is approximately the size of one bioreporter cell, also in individual live bacteria. This is, to our knowledge, the first ever proof-of-concept work utilizing instrumentation with single-molecule detection capability to monitor bioreporter response. Although we use arsenic inducible bioreporters here, the method is extensible to gfp/egfp bioreporters that are responsive to other substances.     

Development and testing of a detection method for liquid chromatography based on aerosol charging

Aerosol-based detection methods for HPLC in which HPLC effluent is converted to an aerosol and detected optically have been employed in the past. This paper describes a new aerosol-based detection method for HPLC, which we name aerosol charge detection. This detection method also involves generation of an aerosol but with aerosol detection by charging aerosol particles and measuring the current from the charged particle flux. A commercial electrical aerosol size analyzer was used for the aerosol detection. The constructed detector was tested using flow injection analysis with water as the mobile phase, and the signal response was found to be linear for sodium sulfate over the concentration ranges of 0.2-100 microg mL(-1) using one of the nebulizers. Minimum mass and concentration detection limits using the more efficient nebulizer were estimated to be 0.2 ng and 10 ng mL(-1), respectively. Behavior for most of the other compounds tested was similar with some differences in sensitivity. Testing the detector using reversed phase HPLC for glucose gave a range of linear response and detection limits that were similar to the flow injection analysis studies. Under most HPLC conditions, the noise will primarily be a function of solvent impurities; however, the electrical aerosol size analyzer allows the removal of small charged particles to improve the signal-to-noise ratio.     

Tabu search algorithm for flexible flow path design of unidirectional automated-guided vehicle systems

The unidirectional flow path design problem is one of the most important but difficult problems for the efficient design of automated-guided vehicle systems. As the problem was first formulated by Gaskins and Tanchoco, many researchers have studied the problem. However, the existing solution methods fail to provide an efficient solution approach. In this paper, a mathematical model for the unidirectional flow path design problem is developed. To obtain a near-to-optimal solution in reasonable computation time, a tabu search algorithm is presented. A fast construction algorithm first obtains a feasible initial solution, and a long-term memory structure and a neighbor solution generation approach are adapted to the problem characteristics and embedded in the proposed tabu search algorithm. Computational experiments show that the developed tabu search algorithm outperforms the Ko and Egbelu’s algorithm, Int J Prod Res, 41:2325–2343, (2003).     

Constrained quadratic correlation filters for target detection

A method for designing and implementing quadratic correlation filters (QCFs) for shift-invariant target detection in imagery is presented. The QCFs are quadratic classifiers that operate directly on the image data without feature extraction or segmentation. In this sense the QCFs retain the main advantages of conventional linear correlation filters while offering significant improvements in other respects. Not only is more processing required for detection of peaks in the outputs of multiple linear filters but choosing the most suitable among them is an error-prone task. All channels in a QCF work together to optimize the same performance metric and to produce a combined output that leads to considerable simplification of the postprocessing scheme. The QCFs that are developed involve hard constraints on the output of the filter. Inasmuch as this design methodology is indicative of the synthetic discriminant function (SDF) approach for linear filters, the filters that we develop here are referred to as quadratic SDFs (QSDFs). Two methods for designing QSDFs are presented, an efficient architecture for achieving them is discussed, and results from the Moving and Stationary Target Acquisition and Recognition synthetic aperture radar data set are presented.     

Parallel load‐balanced simulation for short‐range interaction particle methods with hierarchical particle grouping based on orthogonal recursive bisection

We describe an efficient load‐balancing algorithm for parallel simulations of particle‐based discretization methods such as the discrete element method or smoothed particle hydrodynamics. Our approach is based on an orthogonal recursive bisection of the simulation domain that is the basis for recursive particle grouping and assignment of particle groups to the parallel processors. Particle grouping is carried out based on sampled discrete particle distribution functions. For interaction detection and computation, which is the core part of particle simulations, we employ a hierarchical pruning algorithm for an efficient exclusion of non‐interacting particles via the detection of non‐overlapping bounding boxes. Load balancing is based on a hierarchical PI‐controller approach, where the differences of processor per time step waiting times serve as controller input. Copyright © 2007 John Wiley & Sons, Ltd.     

Aeroacoustic source term computation based on radial basis functions

In low Mach number aeroacoustics, the known disparity of length scales makes it possible to apply well-suited simulation models using different meshes for flow and acoustics. The workflow of these hybrid methodologies include performing an unsteady flow simulation, computing the acoustic sources, and simulating the acoustic field. Therefore, hybrid methods seek for robust and flexible procedures, providing a conservative mesh to mesh interpolation of the sources while ensuring high computational efficiency. We propose a highly specialized radial basis function interpolation for the challenges during hybrid simulations. First, the computationally efficient local radial basis function interpolation in conjunction with a connectivity-based neighbor search technique is presented. Second, we discuss the computation of spatial derivatives based on radial basis functions. These derivatives are computed in a local-global approach, using a Gaussian kernel on local point stencils. Third, radial basis function interpolation and derivatives are used to compute complex aeroacoustic source terms. These ingredients are necessary to provide flexible source term calculations that robustly connect flow and acoustics. Finally, the capabilities of the presented approach are shown in a numerical experiment with a co-rotating vortex pair.     

A Combined Chemical and Electrochemical Approach Using Bis(trifluoroacetoxy)iodobenzene and Glucose Oxidase for the Detection of Chlorinated Phenols

A novel electrocatalytic approach using a chemical reaction and an enzymatic reaction has been developed for the measurement of 18 chlorophenol congeners, including highly chlorinated pollutants such as pentachlorophenol, 2,3,5,6-tetrachlorophenol, 2,3,4,6-tetrachlorophenol, and several trichlorophenols. Chlorophenols were oxidized to chlorobenzoquinones with very high yields using bis(trifluoroacetoxy)iodobenzene in 0.1 M trichloroacetic acid, pH 1.5, at ambient temperature. UV-visible spectrophotometry, cyclic voltammetry, and HPLC have been used to characterize the reaction products and yields. Together with glucose oxidase immobilized on a working glassy carbon electrode (+0.45 V vs Ag/AgCl), chlorinated benzoquinones have been demonstrated to be efficient mediators in a glucose oxidase/glucose system. In this approach, glucose oxidase was readily reduced by excess glucose to provide a non-rate-limiting source of electron flow toward the electrode. The oxidation products of chlorophenols then recycled the reduced glucose oxidase to its active oxidative state, i.e., mediating the rate-limiting electron transfer from the enzyme to the electrode. At pH 3.5, linear behavior of the current response was observed up to 200 nM for all chlorophenol oxidation products. The detection limit of this method for both pentachlorophenol and 2,3,5,6-tetrachlorophenol was about 4 nM, which is close to the maximum allowable contamination level of pentachlorophenol in water samples (2.7 nM). The detection limit obtained for pentachlorophenol could also be considered superior to the result obtained with the PCP immunoassay technology (13.3 nM).     

An Efficient Hybrid Model for Arabic Text Recognition

In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing. The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works. In this work, we presented an efficient and new system for offline Arabic handwritten text recognition. Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term Memory (BLSTM) followed by a Connectionist Temporal Classification layer (CTC). Moreover, during the training phase of the model, we introduce an algorithm of data augmentation to increase the quality of data. Our proposed approach can recognize Arabic handwritten texts without the need to segment the characters, thus overcoming several problems related to this point. To train and test (evaluate) our approach, we used two Arabic handwritten text recognition databases, which are IFN/ENIT and KHATT. The Experimental results show that our new approach, compared to other methods in the literature, gives better results.     

Mass spectrometric methods for generation of protein mass database used for bacterial identification

The availability of a suitable database is critical in a proteomic approach for bacterial identification by mass spectrometry (MS). The major limitation of the present public proteome database is the lack of extensive low-mass bacterial protein entries with masses experimentally verified for most bacteria. Here, we present a method based on mass spectrometry to create protein mass tables specifically tailored for bacterial identification. Several issues related to the detection of bacterial proteins for the purpose of database creation are addressed. Three species of bacteria, namely, Escherichia coli, Bacillus megaterium, and Citrobacter freundii, which can be found in the ambient environment, were chosen for this study. Direct matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS analysis of each bacterial extract reveals 20-29 protein components in the mass range from 2000 to 20,000 Da. HPLC fractionation of bacterial extracts followed by off-line MALDI-TOF analysis of individual fractions detects 156-423 components. Analysis of the extracts by HPLC/electrospray ionization MS shows the number of detectable proteins in the range of 46-59. Although a number of components were common to the three detection schemes employed, some unique components were found using each of these techniques. In addition, for E. coli where a large proteome database exists in the public domain, a number of masses detected by the mass spectrometric methods do not match with the proteome database. Compared to the public proteome database, the mass tables generated in this work are demonstrated to be more useful for bacterial identification in an application where the bacteria of interest have limited protein entries in the public database. The implication of this work for future development of a comprehensive mass database is discussed.     

Efficient representation of turbulent flows using data-enriched finite elements

Efficient modal decomposition of high-dimensional turbulent flow data is an important first step for data reduction, analysis, and low-dimensional predictive modeling. The conventional modal decomposition techniques, such as proper orthogonal and dynamic mode decompositions, aim to represent the system response using spatially global basis vectors that span a broad spatial domain. A significant challenge facing approaches based on global domain decomposition is the rapid increase in both the amount of training data and the number of modes that must be retained for an accurate representation of convection dominated turbulent flows. An alternative generalized finite element (GFEM) based approach is explored for efficient representation of high-dimensional fluid flow data. Here, the standard finite element interpolation method is enriched with numerical functions that are learned from a small amount of high-fidelity training data over spatially localized subdomains. The GFEM approach is demonstrated on a 3D flow past a cylinder at Reynolds number of 100 000 and flows inside a 2D lid-driven cavity over a range of Reynolds numbers. Compared with a global proper orthogonal decomposition, the GFEM-based approach increases efficiency in reconstructing the datasets while also substantially reducing the amounts of training data.     

Adsorptive stripping voltammetry of hen-egg-white-lysozyme via adsorption-desorption at an array of liquid-liquid microinterfaces

Electrochemical adsorption and voltammetry of hen-egg-white-lysozyme (HEWL) was studied at an array of microinterfaces between two immiscible electrolyte solutions (μITIES). Adsorption of the protein was achieved at an optimal applied potential of 0.95 V, after which it was desorbed by a voltammetric scan to lower potentials. The voltammetric peak recorded during the desorption scan was dependent on the adsorption time and on the aqueous phase concentration of HEWL. The slow approach to saturation or equilibrium indicated that protein reorganization at the interface was the rate-determining step and not diffusion to the interface. For higher concentrations and longer adsorption times, a HEWL multilayer surface coverage of 550 pmol cm(-2) was formed, on the basis of the assumption that a single monolayer corresponded to a surface coverage of 13 pmol cm(-2). Implementation of adsorption followed by voltammetric detection as an adsorptive stripping voltammetric approach to HEWL detection demonstrated a linear dynamic range of 0.05-1 μM and a limit of detection of 0.03 μM, for 5 min preconcentration in unstirred solution; this is a more than 10-fold improvement over previous HEWL detection methods at the ITIES. These results provide the basis for a new analytical approach for label-free protein detection based on adsorptive stripping voltammetry.     

Detection and identification of Coxiella burnetii based on the mass spectrometric analyses of the extracted proteins

Rapid and reliable detection, identification, and typing of bacterial species are necessary in response to natural or terrorist-caused outbreaks of infectious diseases and play crucial roles in diagnosis and efficient treatment. We report here two proteomic approaches with a high potential in the detection and identification of Coxiella burnetii, the causative agent of Q fever. The first of them starts with the acetonitrile (ACN) and trichloroacetic acid extractions of inactivated C. burnetii cells followed by the detection of extracted molecules and ions derived from the inactivated cells by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. In the second approach, identification of the proteins extracted by ACN is accomplished after enzymatic digestion by electrospray tandem mass spectrometry coupled to a nanoscale ultraperformance liquid chromatography (LC-MS/MS). In order to observe morphological differences on the surface structures upon extraction, the inactivated and treated cells of the bacterium were examined by electron microscopy. The LC-MS/MS approach has allowed identification of 20 proteins in the ACN extracts of C. burnetii strain RSA 493 that were observed in more than 3 out of 10 experiments.     

Rapid simultaneous ultrasensitive immunodetection of five bacterial toxins

Rapid ultrasensitive detection of gastrointestinal pathogens presents a great interest for medical diagnostics and epidemiologic services. Though conventional immunochemical and polymerase chain reaction (PCR)-based methods are sensitive enough for many applications, they usually require several hours for assay, whereas as sensitive but more rapid methods are needed in many practical cases. Here, we report a new microarray-based analytical technique for simultaneous detection of five bacterial toxins: the cholera toxin, the E. coli heat-labile toxin, and three S. aureus toxins (the enterotoxins A and B and the toxic shock syndrome toxin). The assay involves three major steps: electrophoretic collection of toxins on an antibody microarray, labeling of captured antigens with secondary biotinylated antibodies, and detection of biotin labels by scanning the microarray surface with streptavidin-coated magnetic beads in a shear-flow. All the stages are performed in a single flow cell allowing application of electric and magnetic fields as well as optical detection of microarray-bound beads. Replacement of diffusion with a forced transport at all the recognition steps allows one to dramatically decrease both the limit of detection (LOD) and the assay time. We demonstrate here that application of this "active" assay technique to the detection of bacterial toxins in water samples from natural sources and in food samples (milk and meat extracts) allowed one to perform the assay in less than 10 min and to decrease the LOD to 0.1-1 pg/mL for water and to 1 pg/mL for food samples.     

Regularized learning framework in the estimation of reflectance spectra from camera responses

For digital cameras, device-dependent pixel values describe the camera's response to the incoming spectrum of light. We convert device-dependent RGB values to device- and illuminant-independent reflectance spectra. Simple regularization methods with widely used polynomial modeling provide an efficient approach for this conversion. We also introduce a more general framework for spectral estimation: regularized least-squares regression in reproducing kernel Hilbert spaces (RKHS). Obtained results show that the regularization framework provides an efficient approach for enhancing the generalization properties of the models.     

Fluorescent detection of ATP based on signaling DNA aptamer attached silica nanoparticles

Novel methods for rapid, sensitive and low-cost biomolecule detection have attracted particular interest because of their wide use in medical diagnostics, food inspection and biomedical research applications. In this work, we report a simple and efficient silica nanoparticle (NP)-based fluorescent assay for ATP detection. It takes advantage of the washing and separation properties of NPs and the structure-switch property of DNA aptamers, resulting in fluorescence change of the supernatant in the presence of targets. A linear response for ATP detection was observed from 0 to 6?mM with a detection limit of ～34?μM. This detection strategy could be generalized to other aptamer-based detection systems.     

Automated defect detection system using wavelet packet frame and Gaussian mixture model

This paper proposes an approach for automated defect detection in homogeneous textiles using texture analysis. The texture features are extracted by the wavelet packet frame decomposition followed by the Karhunen-Loève transform. The texture feature vector for each pixel is used as an input to a Gaussian mixture model that determines whether or not each pixel is defective. The parameters of the Gaussian mixture model are estimated with nondefective textile images in supervised defect detection. An approach for unsupervised defect detection is also presented that can identify the heterogeneous subblocks on the basis of the Kullback-Leibler divergence between two Gaussian mixtures. The proposed method was evaluated on 25 different homogeneous textile image pairs, one of each pair with a defect and the other with no defect, and was compared with existing methods using texture analysis. The experimental results yielded visually good segmentation and an excellent detection rate with a low false alarm rate for both supervised and unsupervised defect detection. This confirms the validity of the proposed approach for automated defect detection and localization.     

Fault detection via nonlinear profile monitoring using artificial neural networks

Fault detection is the characterization of a normal behavior of a system using a response function or profile of interest and the identification of any deviation from such normal behavior. As system complexity grows, predicting the underlying structure or form of response function becomes challenging if not impossible. This article presents a data‐driven approach for fault detection of complex systems using multivariate statistical process control based on artificial neural network (ANN) characterization. In this approach, the quality of a system is characterized where one explanatory variable is adequately explained as a function of the other variables using an ANN model. The vector of weights and biases of the ANN model is monitored by using Hotelling T ² through control charts. The proposed method is tested and compared with existing methods such as polynomial and sum of sine function regression for 3 cases from the literature. Moreover, it is applied to a 4‐story reinforced concrete building that uses continuous monitoring to avoid potentially catastrophic failures. The proposed ANN approach outperforms the existing methods for small shifts (deviations) from healthy states. For large and medium shifts, it provides comparable results that are on the conservative side.     

Fibre optic biosensors with immobilized bioluminescence enzymes

A fibre optic biosensor involving immobilized bioluminescence enzymes associated with a glass fibre bundle has been designed. The firefly luciferase fromPhotinus pyralis, as well as the bacterial luciferase-oxidoreductase system from eitherVibrio fischeri orVibrio harveyi, have been immobilized on preactivated polyamide membranes. First, the validity of the approach is demonstrated by performing the microdetermination of adenosine triphosphate (ATP) and sodium dehydrogenase (NADH) in a batch system. The detection limits are equal to 0.1nm for ATP and 0.3nm for NADH. With the NADH-based system, the extension of the biosensor potential to other analytes (ethanol, sorbitol and oxaloacetate) has been achieved using suitable dehydrogenases co-immobilized with the bacterial system. Second, the fibre optic biosensor is associated with a specially designed flow cell for the continuous-flow bioluminescent assay of NADH over the range 2 pmol-1 nmol with an r.s.d. of 3.4% at 0.1 nmol. Finally, a multifunction biosensor for the determination of either ATP or NADH using a single bioluminescence-based fibre optic probe is described. This was made possible by co-immobilizing the firefly luciferase with the bacterial system on the same preactivated polyamide membrane.     

Ceramic nanopatterned surfaces to explore the effects of nanotopography on cell attachment

Surfaces with ordered, nanopatterned roughness have demonstrated considerable promise in directing cell morphology, migration, proliferation, and gene expression. However, further investigation of these phenomena has been limited by the lack of simple, inexpensive methods of nanofabrication. Here, we report a facile, low-cost nanofabrication approach based on self-assembly of a thin-film of gadolinium-doped ceria on yttria-stabilized zirconia substrates (GDC/YSZ). This approach yields three distinct, randomly-oriented nanofeatures of variable dimensions, similar to those produced via polymer demixing, which can be reproducibly fabricated over tens to hundreds of microns. As a proof-of-concept, we examined the response of SK-N-SH neuroblastoma cells to features produced by this system, and observed significant changes in cell spreading, circularity, and cytoskeletal protein distribution. Additionally, we show that these features can be imprinted into commonly used rigid hydrogel biomaterials, demonstrating the potential broad applicability of this approach. Thus, GDC/YSZ substrates offer an efficient, economical alternative to lithographic methods for investigating cell response to randomly-oriented nanotopographical features.     

Eigenanalysis and reduced-order modelling of unsteady partial cavity flows using the boundary element method

This paper presents an efficient reduced-order modelling approach to predict unsteady behaviour of partial cavity flows (PCROM). The boundary element method (BEM) along with the potential flow is used to analyze unsteady partial cavity flows. Partial cavity flow is modelled based on a new non-iterative approach and the PCROM is based on fluid eigenmodes. To construct fluid eigenmodes the spatial iterative scheme to find cavity extent is removed. The eigenvalue problem of the unsteady flows is defined based on the unknown wake singularities. Eigenanalysis and reduced-order modelling of unsteady flows over a NACA 16-006 section are performed using the PCROM. Numerical examples are presented to demonstrate the accuracy of the proposed method. Comparison between the obtained results of the proposed method and those of other and conventional method indicates that the present algorithm works well with sufficient accuracy. Moreover, it is shown that the PCROM is computationally more efficient than the conventional one for unsteady sheet cavitations analysis on hydrofoils.