Impedance Biosensors for the Rapid Detection of Viral and Bacterial Pathogens Using Avian Influenza Virus Subtypes H5N1 and H7N2 and Escherichia coli O157:H7 as Model Targets

This research investigated impedance biosensors for the rapid detection of viral and bacterial pathogens using avian influenza virus (AIV) subtypes H5N1 and H7N2 and Escherichia coli O157:H7 as the model targets, which were chosen due to their impact on the agricultural and food industries. For the detection of AIV H7N2, a single stranded DNA aptamer was selected using systematic evolution of ligands by exponential enrichment (SELEX). The selected aptamer and a previously selected aptamer against AIV H5N1 were used in a microfluidics chip with an embedded interdigitated array microelectrode to fabricate an impedance biosensor for specific detection of AIV H7N2 and H5N1. The developed label-free biosensor was capable of detecting AIV H7N2 and H5N1 at a concentration down to 27×10-4 hemagglutinination units (HAU) in 30 min without sample pre-treatment, comparable to previously designed biosensors though with the advantage of DNA aptamers. Two impedance biosensors based on the use of screen-printed interdigitated electrodes were developed for the detection of E. coli O157:H7. The first was a label-free biosensor based on magnetic separation and concentration of target bacteria using antibody-labelled magnetic nanobeads and Faradic impedance measurement. It was capable of detecting 1400 cells or more of E. coli O157:H7 in a total detection time of 1 h. COMSOL Multiphysics software was used to analyze the biosensor using a simplified model and determine the role of the magnetic nanobeads in the impedance measurement. The second biosensor for detection of E. coli O157:H7 was based on aptamer-labeled magnetic nanobeads and glucose oxidase/Concanavalin A-coated gold nanoparticle labels. This biosensor was capable of detecting 8 cells or more of E. coli O157:H7 in 1.5 h. The lower detection limit of the developed impedance biosensor was comparable to the most sensitive biosensors published for the detection of E. coli O157:H7 and was also more rapid and more practical for in-field tests. Multiple impedance biosensor designs were developed in this research. The developed biosensor for AIV could conceivably be adapted for detection of other AIV subtypes and the developed E. coli O157:H7 biosensors could easily be adapted to detect different bacterial pathogens

Removing the influence of a group variable in high-dimensional predictive modelling

In many application areas, predictive models are used to support or make important decisions. There is increasing awareness that these models may contain spurious or otherwise undesirable correlations. Such correlations may arise from a variety of sources, including batch effects, systematic measurement errors, or sampling bias. Without explicit adjustment, machine learning algorithms trained using these data can produce poor out-of-sample predictions which propagate these undesirable correlations. We propose a method to pre-process the training data, producing an adjusted dataset that is statistically independent of the nuisance variables with minimum information loss. We develop a conceptually simple approach for creating an adjusted dataset in high-dimensional settings based on a constrained form of matrix decomposition. The resulting dataset can then be used in any predictive algorithm with the guarantee that predictions will be statistically independent of the group variable. We develop a scalable algorithm for implementing the method, along with theory support in the form of independence guarantees and optimality. The method is illustrated on some simulation examples and applied to two case studies: removing machine-specific correlations from brain scan data, and removing race and ethnicity information from a dataset used to predict recidivism. That the motivation for removing undesirable correlations is quite different in the two applications illustrates the broad applicability of our approach.Comment: Update. 18 pages, 3 figure

The Cyclic Connectivity of Homogeneous Arcwise Connected Continua

A continuum is cyclicly connected provided each pair of its points lie together on some simple closed curve. In 1927, G. T. Whyburn proved that a locally connected plane continuum is cyclicly connected if and only if it contains no separating points. This theorem was fundamental in his original treatment of cyclic element theory. Since then numerous authors have obtained extensions of Whyburn\u27s theorem. In this paper we characterize cyclic connectedness in the class of all Hausdorff continua

Microprocessor-based digital correlator

We describe the design, construction, and operation of a low-cost, microprocessor-based digital correlator. The device has 128 channels, operates in either the single clipping or single scaling mode, and allows selection of the sample interval with 2-digit precision over the range 100 ns to 9.9 s. The device can be operated in the autocorrelate or cross-correlate mode and may easily be expanded to more than 128 correlation channels

The Dopamine Receptor D4 Gene (DRD4) and Self-Reported Risk Taking in the Economic Domain

Recent evidence suggests that individual variation in risk taking is partly due to genetic factors. We explore how self-reported risk taking in different domains correlates with variation in the dopamine receptor D4 gene (DRD4). Past studies conflict on the influence of DRD4 in relation to risk taking. A sample of 237 serious tournament contract bridge players, experts on risk taking in one domain, was genotyped for having a 7-repeat allele (7R+) or not (7R-) at RD4. No difference was found between 7R+ and 7R- individuals in general risk taking or in several other risk-related activities.

Dopamine and Risk Preferences in Different Domains

Individuals differ significantly in their willingness to take risks. Such differences may stem, at least in part, from individual biological (genetic) differences. We explore how risk-taking behavior correlates with different versions of the dopamine receptor D4 gene (DRD4), which has been implicated in previous studies of risk taking. We investigate risk taking in three contexts: economic risk taking as proxied by a financial gamble, self-reported general risk taking, and self-reported behavior in risk-related activities. Our participants are serious tournament bridge players with substantial experience in risk taking. Presumably, this sample is much less varied in its environment than a random sample of the population, making genetic based differences easier to detect. A prior study (Dreber et al. 2010) looked at risk taking by these individuals in their bridge decisions. Here we examine the riskiness of decisions they take in other contexts. We find evidence that individuals with a 7-repeat allele (7R+) of DRD4 take significantly more economic risk in an investment game than individuals without this allele (7R-). Interestingly, this positive relationship is driven by the men in our study, while the women show a negative but non-significant result. Even though the number of 7R+ women in our sample is low, our results may indicate a gender difference in how the 7R+ genotype affects behavior, a possibility that merits further study. Considering other risk measures, we find no difference between 7R+ and 7R- individuals in general risk taking or any of the risk-related activities. Overall, our results indicate that the dopamine system plays an important role in explaining individual differences in economic risk taking in men, but not necessarily in other activities involving risk.Risk preferences; Dopamine; Risk taking; Risk perception; DRD4

Dopamine and Risk Preferences in Different Domains

Individuals differ significantly in their willingness to take risks. Such differences may stem, at least in part, from individual biological (genetic) differences. We explore how risk-taking behavior varies with different versions of the dopamine receptor D4 gene (DRD4), which has been implicated in previous studies of risk taking. We investigate risk taking in three contexts: economic risk taking as proxied by a financial gamble, self-reported general risk taking, and self-reported behavior in risk-related activities. Our participants are serious tournament bridge players with substantial experience in risk taking. Presumably, this sample is much less varied in its environment than a random sample of the population, making genetic-related differences easier to detect. A prior study (Dreber et al. 2010) looked at risk taking by these individuals in their bridge decisions. We examine their risk decisions in other contexts. We find evidence that individuals with a 7-repeat allele (7R+) of the DRD4 genetic polymorphism take significantly more economic risk in an investment game than individuals without this allele (7R-). Interestingly, this positive relationship is driven by the men in our study, while the women show a negative but non-significant result. Even though the number of 7R+ women in our sample is low, our results may indicate a gender difference in how the 7R+ genotype affects behavior, a possibility that merits further study. Considering other risk measures, we find no difference between 7R+ and 7R- individuals in general risk taking or any of the risk-related activities. Overall, our results indicate that the dopamine system plays an important role in explaining individual differences in economic risk taking in men, but not necessarily in other activities involving risk.

Topological data analysis of Escherichia coli O157:H7 and non-O157 survival in soils.

Shiga toxin-producing E. coli O157:H7 and non-O157 have been implicated in many foodborne illnesses caused by the consumption of contaminated fresh produce. However, data on their persistence in soils are limited due to the complexity in datasets generated from different environmental variables and bacterial taxa. There is a continuing need to distinguish the various environmental variables and different bacterial groups to understand the relationships among these factors and the pathogen survival. Using an approach called Topological Data Analysis (TDA); we reconstructed the relationship structure of E. coli O157 and non-O157 survival in 32 soils (16 organic and 16 conventionally managed soils) from California (CA) and Arizona (AZ) with a multi-resolution output. In our study, we took a community approach based on total soil microbiome to study community level survival and examining the network of the community as a whole and the relationship between its topology and biological processes. TDA produces a geometric representation of complex data sets. Network analysis showed that Shiga toxin negative strain E. coli O157:H7 4554 survived significantly longer in comparison to E. coli O157:H7 EDL 933, while the survival time of E. coli O157:NM was comparable to that of E. coli O157:H7 EDL 933 in all of the tested soils. Two non-O157 strains, E. coli O26:H11 and E. coli O103:H2 survived much longer than E. coli O91:H21 and the three strains of E. coli O157. We show that there are complex interactions between E. coli strain survival, microbial community structures, and soil parameters