Search

The ILSI Research Foundation convened a cross-disciplinary working group to examine current approaches for assessing dose-response and identifying safe levels of intake or exposure for four categories of bioactive agents-food allergens, nutrients, pathogenic microorganisms, and environmental chemicals. This effort generated a common analytical framework-the Key Events Dose-Response Framework (KEDRF)-for systematically examining key events that occur between the initial dose of a bioactive agent and the effect of concern. Individual key events are considered with regard to factors that influence the dose-response relationship and factors that underlie variability in that relationship. This approach illuminates the connection between the processes occurring at the level of fundamental biology and the outcomes observed at the individual and population levels. Thus, it promotes an evidence-based approach for using mechanistic data to reduce reliance on default assumptions, to quantify variability, and to better characterize biological thresholds. This paper provides an overview of the KEDRF and introduces a series of four companion papers that illustrate initial application of the approach to a range of bioactive agents. [ABSTRACT FROM AUTHOR]

This paper presents a behavior-based adaptive mission planner (AMP) to trace a chemical plume to its source and reliably declare the source location. The proposed AMP is implemented on a REMUS autonomous underwater vehicle (AUV) equipped with multiple types of sensors that measure chemical concentration, the flow velocity vector, and AUV position, depth, altitude, attitude, and speed. This paper describes the methods and results from experiments conducted in November 2002 on San Clemente Island, CA, using a plume of Rhodamine dye developed in a turbulent fluid flow (i.e., near-shore ocean conditions). These experiments demonstrated chemical plume tracing over 100 m and source declaration accuracy relative to the nominal source location on the order of tens of meters. The designed maneuvers are divided into four behavior types: finding a plume, tracing the plume, reacquiring the plume, and declaring the source location. The tracing and reacquiring behaviors are inspired by male moths flying upwind along a pheromone plume to locate a sexually receptive female. All behaviors are formulated by perception and action modules and translated into chemical plume-tracing algorithms suitable for implementation on a REMUS AUV. To coordinate the different behaviors, the subsumption architecture is adopted to define and arbitrate the behavior priorities. AUVs capable of such feats would have applicability in searching for environmentally interesting phenomena, unexploded ordnance, undersea wreckage, and sources of hazardous chemicals or pollutants. [ABSTRACT FROM AUTHOR]

Graph classification aims to learn models to classify structure data. To date, all existing graph classification methods are designed to target one single learning task and require a large number of labeled samples for learning good classification models. In reality, each real-world task may only have a limited number of labeled samples, yet multiple similar learning tasks can provide useful knowledge to benefit all tasks as a whole. In this paper, we formulate a new multi-task graph classification (MTG) problem, where multiple graph classification tasks are jointly regularized to find discriminative subgraphs shared by all tasks for learning. The niche of MTG stems from the fact that with a limited number of training samples, subgraph features selected for one single graph classification task tend to overfit the training data. By using additional tasks as evaluation sets, MTG can jointly regularize multiple tasks to explore high quality subgraph features for graph classification. To achieve this goal, we formulate an objective function which combines multiple graph classification tasks to evaluate the informativeness score of a subgraph feature. An iterative subgraph feature exploration and multi-task learning process is further proposed to incrementally select subgraph features for graph classification. Experiments on real-world multi-task graph classification datasets demonstrate significant performance gain. [ABSTRACT FROM PUBLISHER]

Purpose – The purpose of this paper is to improve the performance of E-nose in the detection of wound infection. Feature extraction and selection methods have a strong impact on the performance of pattern classification of electronic nose (E-nose). A new hybrid feature matrix construction method and multi-objective binary quantum-behaved particle swarm optimization (BQPSO) have been proposed for feature extraction and selection of sensor array. Design/methodology/approach – A hybrid feature matrix constructed by maximum value and wavelet coefficients is proposed to realize feature extraction. Multi-objective BQPSO whose fitness function contains classification accuracy and a number of selected sensors is used for feature selection. Quantum-behaved particle swarm optimization (QPSO) is used for synchronization optimization of selected features and parameter of classifier. Radical basis function (RBF) network is used for classification. Findings – E-nose obtains the highest classification accuracy when the maximum value and db 5 wavelet coefficients are extracted as the hybrid features and only six sensors are selected for classification. All results make it clear that the proposed method is an ideal feature extraction and selection method of E-nose in the detection of wound infection. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring, and is beneficial for realizing the clinical application of E-nose. [ABSTRACT FROM AUTHOR]

Background: Optimization procedures to identify gene knockouts for targeted biochemical overproduction have been widely in use in modern metabolic engineering. Flux balance analysis (FBA) framework has provided conceptual simplifications for genome-scale dynamic analysis at steady states. Based on FBA, many current optimization methods for targeted bio-productions have been developed under the maximum cell growth assumption. The optimization problem to derive gene knockout strategies recently has been formulated as a bilevel programming problem in OptKnock for maximum targeted bio-productions with maximum growth rates. However, it has been shown that knockout mutants in fact reach the steady states with the minimization of metabolic adjustment (MOMA) from the corresponding wild-type strains instead of having maximal growth rates after genetic or metabolic intervention. In this work, we propose a new bi-level computational framework- MOMAKnock-which can derive robust knockout strategies under the MOMA flux distribution approximation. Methods: In this new bi-level optimization framework, we aim to maximize the production of targeted chemicals by identifying candidate knockout genes or reactions under phenotypic constraints approximated by the MOMA assumption. Hence, the targeted chemical production is the primary objective of MOMAKnock while the MOMA assumption is formulated as the inner problem of constraining the knockout metabolic flux to be as close as possible to the steady-state phenotypes of wide-type strains. As this new inner problem becomes a quadratic programming problem, a novel adaptive piecewise linearization algorithm is developed in this paper to obtain the exact optimal solution to this new bi-level integer quadratic programming problem for MOMAKnock. Results: Our new MOMAKnock model and the adaptive piecewise linearization solution algorithm are tested with a small E. coli core metabolic network and a large-scale iAF1260 E. coli metabolic network. The derived knockout strategies are compared with those from OptKnock. Our preliminary experimental results show that MOMAKnock can provide improved targeted productions with more robust knockout strategies. [ABSTRACT FROM AUTHOR]

Water–fat separation in magnetic resonance imaging (MRI) is of great clinical importance, and the key to uniform water–fat separation lies in field map estimation. This work deals with three-point field map estimation, in which water and fat are modelled as two single-peak spectral lines, and field inhomogeneities shift the spectrum by an unknown amount. Due to the simplified spectrum modelling, there exists inherent ambiguity in forming field maps from multiple locally feasible field map values at each pixel. To resolve such ambiguity, spatial smoothness of field maps has been incorporated as a constraint of an optimization problem. However, there are two issues: the optimization problem is computationally intractable and even when it is solved exactly, it does not always separate water and fat images. Hence, robust field map estimation remains challenging in many clinically important imaging scenarios. This paper proposes a novel field map estimation technique called JIGSAW. It extends a loopy belief propagation (BP) algorithm to obtain an approximate solution to the optimization problem. The solution produces locally smooth segments and avoids error propagation associated with greedy methods. The locally smooth segments are then assembled into a globally consistent field map by exploiting the periodicity of the feasible field map values. In vivo results demonstrate that JIGSAW outperforms existing techniques and produces correct water–fat separation in challenging imaging scenarios. [ABSTRACT FROM AUTHOR]

This paper demonstrates fundamental limits of sensor networks for detection problems where the number of hypotheses is exponentially large. Such problems characterize many important applications including detection and classification of targets in a geographical area using a network of seismic sensors, and detecting complex substances with a chemical sensor array. We refer to such applications as large-scale detection problems. Using the insight that these problems share fundamental similarities with the problem of communicating over a noisy channel, we define the “sensing capacity” and lower bound it for a number of sensor network models. The sensing capacity expression differs significantly from the channel capacity due to the fact that for a fixed sensor configuration, codewords are dependent and nonidentically distributed. The sensing capacity provides a bound on the minimal number of sensors required to detect the state of an environment to within a desired accuracy. The results differ significantly from classical detection theory, and provide an intriguing connection between sensor networks and communications. In addition, we discuss the insight that sensing capacity provides for the problem of sensor selection. [ABSTRACT FROM PUBLISHER]

Quantitative structure-property relationship (QSPR) modeling is a helpful approach used to correlate the properties of pollutants with their structure descriptors. In this paper a QSPR model for direct photolysis half-lives of polycyclic aromatic hydrocarbons (PAHs) under sunlight on the water surface was developed using density functional theory (DFT) and direct photolysis half-lives of seven PAHs without reported observed values were predicted. The quantum chemical descriptors used in this study were computed at the level of B3LYP/6–311+G(d) and analyzed by partial least squares (PLS) method. The obtained QSPR model with a correlation coefficient of 0.963 was more significant than that derived from semi-empirical molecular orbital algorithm in literatures. It was found that the eigenvalues of the frontier molecular orbital (EHOMO, ELUMO, ENLUMO and ENHOMO) are important in governing the photolysis half-lives of PAHs in water surface, while the molecular weight (MW) and molecular total energy (TE) also have great effects on photolysis half-lives. The importance of ENLUMO and ENHOMO in the model complicates the photolytic mechanism of PAHs and they might become two useful descriptors in QSPR study on photolysis. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for the passive remote sensing of chemical vapours by differential radiometry. The originality of the method lies in the use of a double-input beam Fourier-transform infrared (FTIR) spectrometer optimized for optical subtraction. A description of the interferometer (compact atmospheric sounding interferometer (CATSI)) is given along with the detection algorithm (gaseous emission monitoring algorithm (GASEM)) that controls the interferometer data acquisition and performs the on-line monitoring of chemical vapour parameters. The differential detection method has been successfully tested for several chemical vapours over distances of several hundred metres during open-air experiments held at Defence Research and Development Canada (DRDC). Valcartier and Ft Riley, Kansas. In particular. the detection method has been used to map the integrated concentration (column amount) and the temperature of a plume of methanol vapour. In this case, the uncertainties in the methanol plume parameters have been estimated to he of the order of 15.30% for the column amount. and 2.5 K for the gas temperature, which represents an acceptable result for this type of passive remote sensing. The technique has been applied to ammonia vapours and binary mixtures of the two gases as well. [ABSTRACT FROM AUTHOR]

Chemical mechanical polishing (CMP) is a manufacturing process used to achieve required high levels of global and local planarity, which involves a combination of chemical erosion and mechanical action. The study on mechanical removal action of CMP with hydrodynamic lubrication involved will help us to get some insights into the mechanism of CMP and to solve the lubrication problem of CMP. In this paper, a full approach scheme of multigrid technique incorporated with line relaxation is introduced for accelerating the convergence. The effects of various parameters on load and moments are simulated and the results of computation are reported. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an algorithm that assigns an essentially unique key called the Universal Chemical Key (UCK) to molecular structures. The molecular structures are represented as labeled graphs whose nodes abstract atoms and whose edges abstract bonds. The algorithm was tested on 236,917 compounds obtained from the National Cancer Institute (NCI) database of chemical compounds. On this database, the UCK algorithm assigned unique keys for chemicals with distinct molecular structures. [ABSTRACT FROM AUTHOR]

A conference paper about deterministic clustering of the available chemical space is presented. It informs that the clustering of compound libraries using two-dimensional (2D) binary fingerprints is a fundamental task in chemoinformatics and various methods have been described to solve it. It further informs that the deterministic clustering algorithms, especially agglomerative hierarchical techniques have been shown to yield good results.