Nature Reviews Genetics, Vol. 5, No. 3. (01 March 2004), pp. 169-178.

The genomes of bacterial species show enormous plasticity in the function of individual genes, in genome organization and in regulatory organization. Over millions of years, both bacterial genes and their genomes have been extensively reorganized and adapted so that bacteria occupy virtually every environmental niche on the earth. In addition, changes have occurred in the regulatory circuitry that controls cell operations, cell-cycle progression and responses to environmental signals. The mechanisms that underlie the adaptation of the bacterial regulatory circuitry are crucial for understanding the bacterial biosphere and have important roles in the emergence of antibiotic resistance.
Harley McAdams, Balaji Srinivasan, Adam Arkin

Neurocomputing, Vol. 69, No. 16-18. (October 2006), pp. 1954-1961.

An important character of on-line learning is its potential to adapt to changing environments by properly adjusting meta-parameters that control the balance between plasticity and stability of the learning model. In our previous study, we proposed a learning scheme to address changing environments in the framework of an on-line variational Bayes (VB), which is an effective on-line learning scheme based on Bayesian inference. The motivation of that work was, however, its implications for animal learning, and the formulation of the learning model was heuristic and not theoretically justified. In this article, we propose a new approach that balances the plasticity and stability of on-line VB learning in a more theoretically justifiable manner by employing the principle of hierarchical Bayesian inference. We present a new interpretation of on-line VB as a special case of incremental Bayes that allows the hierarchical Bayesian setting to balance the plasticity and stability as well as yielding a simple learning rule compared to standard on-line VB. This dynamic on-line VB scheme is applied to probabilistic PCA as an example of probabilistic models involving latent variables. In computer simulations using artificial data sets, the new on-line VB learning shows robust performance to regulate the balance between plasticity and stability, thus adapting to changing environments.
J Hirayama, J Yoshimoto, S Ish

Current Biology, Vol. 17, No. 22. (20 November 2007), pp. R977-R980.

Summary Subjects adapt to environmental changes differently depending on the perceived frequency of the changes — the environmental volatility — similar to an ideal Bayesian learner. This volatility information correlates with the fMRI BOLD signal in the anterior cingulate cortex.
Angela Yu

Pharmacological Reviews, Vol. 53, No. 1. (1 March 2001), pp. 1-24.

G protein-coupled receptors (GPCRs) are seven transmembrane proteins that form the largest single family of integral membrane receptors. GPCRs transduce information provided by extracellular stimuli into intracellular second messengers via their coupling to heterotrimeric G proteins and the subsequent regulation of a diverse variety of effector systems. Agonist activation of GPCRs also initiates processes that are involved in the feedback desensitization of GPCR responsiveness, the internalization of GPCRs, and the coupling of GPCRs to heterotrimeric G protein-independent signal transduction pathways. GPCR desensitization occurs as a consequence of G protein uncoupling in response to phosphorylation by both second messenger-dependent protein kinases and G protein-coupled receptor kinases (GRKs). GRK-mediated receptor phosphorylation promotes the binding of β-arrestins, which not only uncouple receptors from heterotrimeric G proteins but also target many GPCRs for internalization in clathrin-coated vesicles. β-Arrestin-dependent endocytosis of GPCRs involves the direct interaction of the carboxyl-terminal tail domain of β-arrestins with both β-adaptin and clathrin. The focus of this review is the current and evolving understanding of the contribution of GRKs, β-arrestins, and endocytosis to GPCR-specific patterns of desensitization and resensitization. In addition to their role as GPCR-specific endocytic adaptor proteins, β-arrestins also serve as molecular scaffolds that foster the formation of alternative, heterotrimeric G protein-independent signal transduction complexes. Similar to what is observed for GPCR desensitization and resensitization, β-arrestin-dependent GPCR internalization is involved in the intracellular compartmentalization of these protein complexes.
Stephen Ferguson

The Journal of Physiology, Vol. 517, No. 1. (May 1999), pp. 5-23.
Moritz Bunemann, Marlene Hosey

Nature Reviews Neuroscience, Vol. 2, No. 10. (01 October 2001), pp. 727-733.

In the classical model of G-protein-coupled receptor (GPCR) regulation, arrestins terminate receptor signalling. After receptor activation, arrestins desensitize phosphorylated GPCRs, blocking further activation and initiating receptor internalization. This function of arrestins is exemplified by studies on the role of arrestins in the development of tolerance to, but not dependence on, morphine. Arrestins also link GPCRs to several signalling pathways, including activation of the non-receptor tyrosine kinase SRC and mitogen-activated protein kinase. In these cascades, arrestins function as adaptors and scaffolds, bringing sequentially acting kinases into proximity with each other and the receptor. The signalling roles of arrestins have been expanded even further with the discovery that the formation of stable receptor–arrestin complexes initiates photoreceptor apoptosis in Drosophila, leading to retinal degeneration. Here we review our current understanding of arrestin function, discussing both its classical and newly discovered roles.
Kristen Pierce, Robert Lefkowitz

Nature reviews. Neuroscience, Vol. 11, No. 4. (April 2010), pp. 239-251.

Synaptic plasticity is thought to underlie learning and memory, but the complexity of the interactions between the ion channels, enzymes and genes that are involved in synaptic plasticity impedes a deep understanding of this phenomenon. Computer modelling has been used to investigate the information processing that is performed by the signalling pathways involved in synaptic plasticity in principal neurons of the hippocampus, striatum and cerebellum. In the past few years, new software developments that combine computational neuroscience techniques with systems biology techniques have allowed large-scale, kinetic models of the molecular mechanisms underlying long-term potentiation and long-term depression. We highlight important advancements produced by these quantitative modelling efforts and introduce promising approaches that use advancements in live-cell imaging.
Jeanette Hellgren Kotaleski, Kim Blackwell

Journal of International Business Studies, Vol. 38, No. 4. (10 July 2007), pp. 519-540.
Daphne Yiu, ChungMing Lau, Garry Bruton

Journal of International Business Studies, Vol. 38, No. 4. (10 July 2007), pp. 673-690.
Lianxi Zhou, Wei-ping Wu, Xueming Luo

Clinica Chimica Acta, Vol. 347, No. 1-2. (September 2004), pp. 25-39.

Breath analysis has attracted a considerable amount of scientific and clinical interest during the last decade. In contrast to NO, which is predominantly generated in the bronchial system, volatile organic compounds (VOCs) are mainly blood borne and therefore enable monitoring of different processes in the body. Exhaled ethane and pentane concentrations were elevated in inflammatory diseases. Acetone was linked to dextrose metabolism and lipolysis. Exhaled isoprene concentrations showed correlations with cholesterol biosynthesis. Exhaled levels of sulphur-containing compounds were elevated in liver failure and allograft rejection. Looking at a set of volatile markers may enable recognition and diagnosis of complex diseases such as lung or breast cancer. Due to technical problems of sampling and analysis and a lack of normalization and standardization, huge variations exist between results of different studies. This is among the main reasons why breath analysis could not yet been introduced into clinical practice. This review addresses the basic principles of breath analysis and the diagnostic potential of different volatile breath markers. Analytical procedures, issues concerning biochemistry and exhalation mechanisms of volatile substances, and future developments will be discussed.
W Miekisch

Biosensors and Bioelectronics, Vol. 14, No. 8-9. (December 1999), pp. 663-670.

A tutorial review article on the quartz crystal microbalance (QCM) is presented. Following an introduction to the theory and commercial aspects of the QCM the authors present a summary of recent and important prior research in each of the different areas of analytical interest in the QCM.
C O'Sullivan

Biosensors and Bioelectronics, Vol. 20, No. 11. (15 May 2005), pp. 2218-2227.

Fish chromatophores have been shown to be promising biosensors for the detection of hostile agents in the environment. However, state-of-art methods for such applications are still based on extensive use of data/signal processing, in conjunction with need for a skilled human observer to carry out the detection. As a result, conventional methods are complex, costly and cumbersome rendering them useless for field applications requiring low-cost portable solutions capable of fast detection. A new technique is proposed based on the popular scheme of observing the aggregation response in chromatophores for detection of toxicity, and a solution using optical detection and electronic processing is outlined. This scheme has the advantage of being low in cost while providing simple, fast and reliable detection.
Vivek Sharma, Arthi Narayanan, Thirumalai Rengachari, Gabor Temes, Frank Chaplen, Un-Ku Moon

Biosensors and Bioelectronics, Vol. 24, No. 9. (15 May 2009), pp. 2749-2765.

The use of nanoscale materials (e.g., nanoparticles, nanowires, and nanorods) for electrochemical biosensing has seen explosive growth in recent years following the discovery of carbon nanotubes by Sumio Ijima in 1991. Although the resulting label-free sensors could potentially simplify the molecular recognition process, there are several important hurdles to be overcome. These include issues of validating the biosensor on statistically large population of real samples rather than the commonly reported relatively short synthetic oligonucleotides, pristine laboratory standards or bioreagents; multiplexing the sensors to accommodate high-throughput, multianalyte detection as well as application in complex clinical and environmental samples. This article reviews the status of biomolecular recognition using electrochemical detection by analyzing the trends, limitations, challenges and commercial devices in the field of electrochemical biosensors. It provides a survey of recent advances in electrochemical biosensors including integrated microelectrode arrays with microfluidic technologies, commercial multiplex electrochemical biosensors, aptamer-based sensors, and metal-enhanced electrochemical detection (MED), with limits of detection in the attomole range. Novel applications are also reviewed for cancer monitoring, detection of food pathogens, as well as recent advances in electrochemical glucose biosensors.
Omowunmi Sadik, Austin Aluoch, Ailing Zhou

Sensors and Actuators B: Chemical, Vol. 89, No. 3. (01 April 2003), pp. 269-284.

This work reports on the performance of a volatile organic compounds (VOCs) identification system based on a surface acoustic wave (SAW) multi- sensor array with four acoustic sensing elements, developed in dual configuration as multiplexed two-port resonator 433.92 MHz oscillators and a reference SAW element, in order to recognize the different individual components in a binary mixture of VOCs such as methanol (CH 3 OH) and 2-propanol (C 3 H 7 OH), in the range 20–140 and 5–70 ppm, respectively. The SAW sensors, operating at room temperature, have been specifically coated by sensing thin films belonging to various chemical classes such as arachidic acid (fatty acids), carbowax (stationary phases), triethanolamine (amines), acrylated polysiloxane (polysiloxanes) to ensure cross-sensitivity towards VOCs under test. By using the relative frequency change as the output signal of the SAW multi- sensor array with an artificial neural network (ANN), a recognition system has been realized for the identification of tested VOCs. The features extraction from output signals of the SAW multi- sensor array, exposed to the binary component mixture of methanol and 2-propanol, has been also performed by pattern recognition techniques such as principal component analysis (PCA). The feedforward multi-layer neural network with a hidden layer and trained by a back-propagation learning algorithm has been implemented in order to classify and identify the tested VOCs patterns. Both the normalized responses of four SAW sensors array and the selected principal components (PCs) scores have been used as inputs to the multi-layer perceptron ANN by resulting in a 100% success recognition rate with the four SAW sensors normalized responses and with the first two principal components scores of the original data of the primary matrix. The different strategies used to recognize the VOCs patterns by the ANNs such as the ‘Leave-one-out’ method and ‘Training-and-Test’ method are discussed. Our experimental results have evidenced that the proposed binary vapor mixture classifier based on the electronic nose system, developed by inexpensive and poorly selective chemical SAW sensors, is highly effective in the identification of tested VOCs of methanol and 2-propanol. Moreover, the combination of PCA, as data pre-processing technique, and ANN, as patterns classification technique, provides a rapid and accurate recognition method of the individual components in the tested binary mixture of methanol and 2-propanol.
M Penza

Sensors and Actuators B: Chemical, Vol. 96, No. 1-2. (15 November 2003), pp. 354-363.

This paper mainly deals with the sensor drift in the application of gas concentration measurement, but little has been done in previous works. The algorithm of detecting the drift of sensors presented in this paper is based on the combination of the principal component analysis (PCA) with the wavelet analysis. By this algorithm the sensor drift can be detected online sensitively. For compensating the drift of sensors, an adaptive dynamic drift compensation algorithm (ADDC) based on a drift model is also provided in this paper. From the drift model, the drift compensation factors used to compensate the drifting sensor’s data are calculated. When the drift feature changed, the drift model will be updated online adaptively. In this way, a lifelong efficient drift compensation is made possible for every sensor. The superior performance of this drift counteraction strategy is illustrated with the examples using real semiconductor sensor array data.

Sensors and Actuators B: Chemical, Vol. 143, No. 2. (07 January 2010), pp. 740-749.

An electronic nose (EN) based on an array of chemiresistors, combined with a preconcentrator unit, for the detection of some volatile organic vapors was developed. In order to choose the proper polymers, seven potential polymers were chosen from numerous available polymers according to the principle of the linear solvation energy relationship (LSER). Different possible sensors arrays (128 arrays) composed of these seven polymers were designed by full factorial design (FFD). Principal component analysis (PCA) showed that four of seven polymers had enough ability to recognize different gas classes. By using Hierarchical cluster analysis (HCA), the tested polymers were categorized into four main groups with respect to their recognition ability. Combination of the FFD with PCA and HCA, brought to the identification of 8 proper arrays containing four polymers in each array. Precisely evaluation of predicted arrays with respect to their calculated resolution factors showed that the electronic nose containing the polymers of 75% pheny125% methylpolysiloxane (OV25), hexafluoro-2-propanolsubstituted polysiloxane (SXFA), poly bis(cyanopropyl)-siloxane (SXCN) and poly(ethylene maleate) (PEM) was the most proper design for recognition of analytes of interest. The fabricated EN was used successively for target gas recognition at three different concentrations.
Taher Alizadeh

Sensors and Actuators B: Chemical, Vol. 140, No. 1. (18 June 2009), pp. 319-336.

Hierarchical and hollow oxide nanostructures are very promising gas sensor materials due to their high surface area and well-aligned nanoporous structures with a less agglomerated configurations. Various synthetic strategies to prepare such hierarchical and hollow structures for gas sensor applications are reviewed and the principle parameters and mechanisms to enhance the gas sensing characteristics are investigated. The literature data clearly show that hierarchical and hollow nanostructures increase both the gas response and response speed simultaneously and substantially. This can be explained by the rapid and effective gas diffusion toward the entire sensing surfaces via the porous structures. Finally, the impact of highly sensitive and fast responding gas sensors using hierarchical and hollow nanostructures on future research directions is discussed.
Jong-Heun Lee

Sensors and Actuators B: Chemical, Vol. 145, No. 2. (19 March 2010), pp. 620-627.

The aim of this paper is to find useful relationships in differential form that describe the isothermal steady state interactions between a sensor array based on metal oxide sensors and a mixture of vapours. These equations of state relate the variation of partial molar intensive quantities (as the change of the sensor molar partial sensitivity or molar adsorptions enthalpy), to gas mixture components concentrations and sensor array parameters. This kind of equalities is known in the thermodynamic of miscellaneous as Gibbs–Duhem equations.
Abdelaziz Abbas, Ahcène Bouabdellah

Sensors and Actuators B: Chemical, Vol. 123, No. 1. (10 April 2007), pp. 437-443.

We perform feature selection (FS) on an electronic nose (EN) dataset composed of 30 features, obtained by extracting 5 diverse features from the response curves of six metal oxide sensors. The 5 features are: the classical relative change in resistance R / R 0 ; the curve integral both over the gas adsorption and desorbtion process and the phase space integral, again over adsorption and desorbtion. The phase space integral is a novel feature introduced in [1] . We show that performance (in terms of the cross validated test error of a three nearest neighbour classifier) is always significantly better for the best selected features than for all 30 features. Moreover – for some of the 5 features types – performance with all 30 features is worse than performance with just the 6 features of a single type. Results are not univocal regarding the best feature type. Yet, on average over the four datasets in which the complete dataset can be decomposed, the phase integral calculated over the desorption wins. Also, the features (phase and integral) calculated on the desorbtion seem to consistently give higher performance than the corresponding features calculated during adsorption. The standard R / R 0 stands in the lower part of the ranking.
M Pardo, G Sberveglieri

Sensors and Actuators B: Chemical, Vol. 52, No. 1-2. (15 September 1998), pp. 125-142.

Perfect ‘chemical imaging’ aims at the time- and spatially-resolved recording of many chemical species. Comparison of results from ‘chemical imaging’ with calibration data may also be trained towards an identification of odor impressions, environmental or medical conditions (such as toxicity), process control parameters etc. This ‘chemical imaging’ can be approached by either using the well-established techniques of analytical chemistry or by using a large number of calibrated sensors and sensor systems. The latter are sometimes denoted ‘electronic noses’, provide an electronic approach to artificial olfaction and are considered in this paper. They offer a variety of principal advantages including the fact that calibration efforts and sizes can be minimized systematically for specific applications by fine-tuning individual components of the sensor system. The paper describes a systematic to design such sensor systems. In the traditional application of chemical sensors the output of an individual chemical sensor is recorded as one ‘feature’. The first aim towards perfect ‘chemical imaging’ is to determine a large number of independent features, which span a large ‘hyperspace of chemical features’. The second aim is then to extract information from this hyperspace by optimizing a feature extraction procedure towards four application-specific goals. (a) The first goal concerns to record certain chemical species quantitatively and hence aims at perfect ‘chemical imaging’ as defined above. (b) Alternative goals concern to record odor impressions, (c) environmental or medical conditions, (d) and process control parameters. Different kinds of calibration are wanted to extract the wanted information from the data represented in the hyperspace of chemical sensor features. Hence, four different strategies are required to compare the features monitored by the chemical sensor systems with independent calibration standards from (a) instruments in analytical chemistry, (b) human odor panels, (c) (micro-)biological or medical tests, (d) and process parameter measurements. This adjustment of measured sensor features to calibration standards determines a specific type of feature extraction and pattern recognition for a specific application. This pattern recognition of experimentally recorded features is of key importance not only for these ‘electronic’ noses but occurs in the same way in all real ‘biological’ noses. Hence, formal analogies between the technical and biological world of noses are obvious. It is therefore expected, that our current studies on chemical sensor systems will also lead to a deeper understanding of signal processing in biological sensor systems and vice versa. Expected synergies of comparative studies concern in particular the molecular scale understanding of (a) the elementary processes of chemical sensing, (b) human odor perception, and (c) interactions between the environment and biological organisms. In this context, biolectronics becomes an increasingly important discipline. By taking advantage of characteristic similarities and differences of components in technical and biological systems, high-performance hybrid systems will be developped in the future.
W Göpel

Sensors and Actuators B: Chemical, Vol. 18, No. 1-3. (March 1994), pp. 259-263.

The paper describes the identification of gases with statistical methods and neural networks. It is shown that there is an optimal standardization of measurement data with regard to prediction accuracy. An example with two sensors and two gases is discussed and the differences of the methods worked out. It is shown in which case neural networks have an advantage over statistical methods. Finally, results of data evaluations with discriminant analyses and neural networks are presented.
G Niebling

Sensors and Actuators B: Chemical, Vol. 114, No. 1. (30 March 2006), pp. 85-93.

A sensor array composed of selective and partially selective electrodes is applied to milk recognition. The task of the system is to distinguish among five brands of milk. For this purpose, five pattern recognition (PARC) procedures are employed: three linear ( K -nearest neighbours, partial least squares, soft independent modelling of class analogy) and two nonlinear (back propagation neural networks and learning vector quantization). Classification accuracy is compared and some analogies with general rules referring to electronic nose were found. LVQ networks are proved to exhibit the best performance. Their further advantages, such as fast training and robustness, make them the suggested pattern classifiers for sensor array data.
Patrycja Ciosek, Wojciech Wróblewski

Sensors and Actuators B: Chemical, Vol. 4, No. 1-2. (May 1991), pp. 109-115.

Mathematical expressions describing the response of individual sensors and arrays of tin oxide gas sensors are derived from a barrier-limited electron mobility model. From these expressions, the fractional change in conductance is identified as the optimal response parameter with which to characterize sensor array performance instead of the more usual relative conductance. In an experimental study, twelve tin oxide gas sensors are exposed to five alcohols and six beverages, and the responses are studied using pattern-recognition methods. Results of regression and supervised learning analysis show a high degree of colinearity in the data with a subset of only five sensors needed for classification. Principal component analysis and clustering methods are applied to the response of the tin oxide sensors to all the vapours. The results show that the theoretically derived normalization of the data set substantially improves the classification of vapours and beverages. The individual alcohols are separated out into five distinct clusters, whereas the beverages cluster into only three distinct classes, namely, beers, lagers and spirits. It is suggested that the separation may be improved further by employing other sensor types or processing techniques.
Julian Gardner

Sensors and Actuators B: Chemical, Vol. 9, No. 1. (July 1992), pp. 9-15.

Considerable interest has recently arisen in the use of arrays of gas sensors together with an associated pattern-recognition technique to identfy vapours and odours. The performance of the pattern-recognition technique depends upon the choice of parametric expression used to define the array output. At present, there is no generally agreed choice of this parameter for either individual sensors or arrays of sensors. In this paper, we have initially performed a parametric study on experimental data gathered from the response of an array of twelve tin oxide gas sensors to five alcohols and three beers. Five parametric expressions of sensor response are used to characterize the array output, namely, fractional conductance change, relative conductance, log of conductance change and normalized versions of the last two expressions. Secondly, we have applied the technique of artificial neural networks (ANNs) to our preprocessed data. The Rumelhart back-propagation technique is used to train all networks. We find that nearly all of our ANNs can correctly identify all the alcohols using our array of twelve tin oxide sensors and so we use the total sum of squared network errors to determine their relative performance. It is found that the lowest network error occurs for the response parameter defined as the fractional change in conductance, with a value of 1.3 × 10 −4 , which is almost half that for the relative conductance. The normalized procedure is also found to improve network performance and so is worthwhile. The optimal network for our data-set is found to contain a single hidden layer of seven elements with a learning rate of 1.0 and momentum term of 0.7, rather than the values of 0.9 and 0.6 recommended by Rumelhart and McClelland, respectively. For this network, the largest output error is less than 0.1. We find that this network outperforms principal-component and cluster analyses (discussed in Part 1) by identifying similar beer odours and offers considerable benefit in its ability to cope with non-linear and highly correlated data.
JW Gardner, EL Hines, HC Tang

J Clin Oncol In J Clin Oncol, Vol. 27, No. 32. (10 November 2009), pp. 5312-5318.

PurposeAn outcome of considerable concern among breast cancer survivors is the development of second primary breast cancer. However, evidence regarding how potentially modifiable lifestyle factors modulate second breast cancer risk is limited. We evaluated the relationships between obesity, alcohol consumption, and smoking on risk of second primary invasive contralateral breast cancer among breast cancer survivors. MethodsUtilizing a population-based nested case-control study design, we enrolled 365 patients diagnosed with an estrogen receptor-positive (ER+) first primary invasive breast cancer and a second primary contralateral invasive breast cancer, and 726 matched controls diagnosed with only an ER+ first primary invasive breast cancer. Obesity, alcohol use, and smoking data were ascertained from medical record reviews and participant interviews. Using conditional logistic regression we evaluated associations between these three exposures and second primary contralateral breast cancer risk. ResultsObesity, consumption of [≥] 7 alcoholic beverages per week, and current smoking were all positively related to risk of contralateral breast cancer (odds ratio [OR], 1.4; 95% CI, 1.0 to 2.1; OR, 1.9; 95% CI, 1.1 to 3.2; and OR, 2.2; 95% CI, 1.2 to 4.0, respectively). Compared with women who consumed fewer than seven alcoholic beverages per week and were never or former smokers, women who consumed [≥] 7 drinks per week and were current smokers had a 7.2-fold (95% CI, 1.9 to 26.5) elevated risk of contralateral breast cancer. ConclusionOur population-based study adds to the limited available literature and suggests that obesity, smoking, and alcohol consumption influence contralateral breast cancer risk, affording breast cancer survivors three means of potentially reducing this risk. 10.1200/JCO.2009.23.1597
Christopher Li, Janet Daling, Peggy Porter, Mei-Tzu Tang, Kathleen Malone