The vertebrate olfactory system has long been recognized for its extraordinary sensitivity and selectivity for odours. Chemical sensors have been developed recently that are based on analogous distributed sensing properties, but although an association between artificial devices and the olfactory system has been made explicit in some previous studies, none has incorporated comparable mechanisms into the mode of detection. Here we describe a multi-analyte fibre-optic sensor modelled directly on the olfactory system, in the sense that complex, time-dependent signals from an array of sensors provide a 'signature' of each analyte. In our system, polymer-immobilized dye molecules on the fibre tips give different fluorescent response patterns (including spectral shifts, intensity changes, spectral shape variations and temporal responses) on exposure to organic vapours, depending on the physical and chemical nature (for example, polarity, shape and size) of both the vapour and the polymer. We use video images of temporal responses of the multi-fibre tip as the input signals to train a neural network for vapour recognition. The system is able to identify individual vapours at different concentrations with great accuracy. 'Artificial noses' such as this should have wide potential application, most notably in environmental and medical monitoring.

We have developed a simple and efficient algorithm to identify each member of a large collection of DNA-linked objects through the use of hybridization, and have applied it to the manufacture of randomly assembled arrays of beads in wells. Once the algorithm has been used to determine the identity of each bead, the microarray can be used in a wide variety of applications, including single nucleotide polymorphism genotyping and gene expression profiling. The algorithm requires only a few labels and several sequential hybridizations to identify thousands of different DNA sequences with great accuracy. We have decoded tens of thousands of arrays, each with 1520 sequences represented at ∼30-fold redundancy by up to ∼50,000 beads, with a median error rate of <1 × 10 −4 per bead. The approach makes use of error checking codes and provides, for the first time, a direct functional quality control of every element of each array that is manufactured. The algorithm can be applied to any spatially fixed collection of objects or molecules that are associated with specific DNA sequences.Microarray technology, devised for the analysis of complex biological systems, uses the ability of a DNA strand to hybridize specifically to its complement to extract 1000s of measurements at a time from a single sample

We report a new approach to designing an artificial nose based on high-density optical arrays that directly incorporate a number of structural and operational features of the olfactory system. The arrays are comprised of thousands of microsphere (bead) sensors, each belonging to a discrete class, randomly dispersed across the face of an etched optical imaging fiber. Beads are recognized and classified after array assembly by their unique, "self-encoded" response pattern to a selected vapor pulse. The high degree of redundancy built into the array parallels that found in nature and affords new opportunities for chemical-sensor signal amplification. Since each bead is independently addressable through its own light channel, it is possible to combine responses from same-type beads randomly distributed throughout the array in a manner reminiscent of the sensory-neuron convergence observed in the mammalian olfactory system. Signal-to-noise improvements of approximately n1/2 have been achieved using this method.

The recent development of whole genome association studies has lead to the robust identification of several loci involved in different common human diseases. Interestingly, some of the strongest signals of association observed in these studies arise from non-coding regions located in very large introns or far away from any annotated genes, raising the possibility that these regions are involved in the etiology of the disease through some unidentified regulatory mechanisms. These findings highlight the importance of better understanding the mechanisms leading to inter-individual differences in gene expression in humans. Most of the existing approaches developed to identify common regulatory polymorphisms are based on linkage/association mapping of gene expression to genotypes. However, these methods have some limitations, notably their cost and the requirement of extensive genotyping information from all the individuals studied which limits their applications to a specific cohort or tissue. Here we describe a robust and high-throughput method to directly measure differences in allelic expression for a large number of genes using the Illumina Allele-Specific Expression BeadArray platform and quantitative sequencing of RT-PCR products. We show that this approach allows reliable identification of differences in the relative expression of the two alleles larger than 1.5-fold (i.e., deviations of the allelic ratio larger than 60∶40) and offers several advantages over the mapping of total gene expression, particularly for studying humans or outbred populations. Our analysis of more than 80 individuals for 2,968 SNPs located in 1,380 genes confirms that differential allelic expression is a widespread phenomenon affecting the expression of 20% of human genes and shows that our method successfully captures expression differences resulting from both genetic and epigenetic cis-acting mechanisms.

We report here the development of a new vapor sensing device that is designed as an array of optically based chemosensors providing input to a pattern recognition system incorporating artificial neural networks. Distributed sensors providing inputs to an integrative circuit is a principle derived from studies of the vertebrate olfactory system. In the present device, primary chemosensing input is provided by an array of fiber-optic sensors. The individual fiber sensors, which are broadly yet differentially responsive, were constructed by immobilizing molecules of the fluorescent indicator dye Nile Red in polymer matrices of varying polarity, hydrophobicity, pore size, elasticity, and swelling tendency, creating unique sensing regions that interact differently with vapor molecules. The fluorescent signals obtained from each fiber sensor in response to 2-s applications of different analyte vapors have unique temporal characteristics. Using signals from the fiber array as inputs, artificial neural networks were trained to identify both single analytes and binary mixtures, as well as relative concentrations. Networks trained with integrated response data from the array or with temporal data from a single fiber made numerous errors in analyte identification across concentrations. However, when trained with temporal information from the fiber array, networks using "name" or "characteristic" output codes performed well in identifying test analytes.