Pseudomonas aeruginosa can be detected in a polymicrobial competition model using impedance spectroscopy with a novel biosensor

Electrochemical Impedance Spectroscopy (EIS) is a powerful technique that can be used to elicit information about an electrode interface. In this article, we highlight six principal processes by which the presence of microorganisms can affect impedance and show how one of these - the production of electroactive metabolites - changes the impedance signature of culture media containing Pseudomonas aeruginosa. EIS, was used in conjunction with a low cost screen printed carbon sensor to detect the presence of P. aeruginosa when grown in isolation or as part of a polymicrobial infection with Staphylococcus aureus. By comparing the electrode to a starting measurement, we were able to identify an impedance signature characteristic of P. aeruginosa. Furthermore, we are able to show that one of the changes in the impedance signature is due to pyocyanin and associated phenazine compounds. The findings of this study indicate that it might be possible to develop a low cost sensor for the detection of P. aeruginosa in important point of care diagnostic applications. In particular, we suggest that a development of the device described here could be used in a polymicrobial clinical sample such as sputum from a CF patient to detect P. aeruginosa

Controlling the False Discovery Rate in Astrophysical Data Analysis

The False Discovery Rate (FDR) is a new statistical procedure to control the number of mistakes made when performing multiple hypothesis tests, i.e. when comparing many data against a given model hypothesis. The key advantage of FDR is that it allows one to a priori control the average fraction of false rejections made (when comparing to the null hypothesis) over the total number of rejections performed. We compare FDR to the standard procedure of rejecting all tests that do not match the null hypothesis above some arbitrarily chosen confidence limit, e.g. 2 sigma, or at the 95% confidence level. When using FDR, we find a similar rate of correct detections, but with significantly fewer false detections. Moreover, the FDR procedure is quick and easy to compute and can be trivially adapted to work with correlated data. The purpose of this paper is to introduce the FDR procedure to the astrophysics community. We illustrate the power of FDR through several astronomical examples, including the detection of features against a smooth one-dimensional function, e.g. seeing the ``baryon wiggles'' in a power spectrum of matter fluctuations, and source pixel detection in imaging data. In this era of large datasets and high precision measurements, FDR provides the means to adaptively control a scientifically meaningful quantity -- the number of false discoveries made when conducting multiple hypothesis tests.Comment: 15 pages, 9 figures. Submitted to A

Development of a diagnostic device to detect different pseudomonas aeruginosa phenotypes in medically relevant contexts

Pseudomonas aeruginosa, widely present in the environment, is well known for its ability to cause infection in immune compromised individuals. For example, P. aeruginosa is the leading cause of morbidity and mortality in patients with cystic fibrosis (CF). Here, we describe how Electrochemical Impedance Spectroscopy (EIS) can be used to detect the presence of four different strains of P. aeruginosa. Using a low cost, screen printed carbon electrode significant changes can be seen in impedance data in the presence of P. aeruginosa after 24 hours. Furthermore, through the use of a normalization technique whereby the phase angle of the impedance (a commonly used parameter) is divided by a starting measurement, it is possible to identify differences between a non-mucoid and mucoid strain of P. aeruginosa. Sensors based upon the techniques described here could be used in a number of healthcare scenarios, where there is a need for low cost, real time detection of P. aeruginosa, such as CF

Spatially Resolved Galaxy Star Formation and its Environmental Dependence I

We use the photometric information contained in individual pixels of 44,964 (0.019<z<0.125 and -23.5<M_r<-20.5) galaxies in the Fourth Data Release (DR4) of the Sloan Digital Sky Survey to investigate the effects of environment on galaxy star formation (SF). We use the pixel-z technique, which combines stellar population synthesis models with photometric redshift template fitting on the scale of individual pixels in galaxy images. Spectral energy distributions are constructed, sampling a wide range of properties such as age, star formation rate (SFR), dust obscuration and metallicity. By summing the SFRs in the pixels, we demonstrate that the distribution of total galaxy SFR shifts to lower values as the local density of surrounding galaxies increases, as found in other studies. The effect is most prominent in the galaxies with the highest star formation, and we see the break in the SFR-density relation at a local galaxy density of $\approx 0.05$(Mpc/h)$^{-3}$. Since our method allows us to spatially resolve the SF distribution within galaxies, we can calculate the mean SFR of each galaxy as a function of radius. We find that on average the mean SFR is dominated by SF in the central regions of galaxies, and that the trend for suppression of SFR in high density environments is driven by a reduction in this nuclear SF. We also find that the mean SFR in the outskirts is largely independent of environmental effects. This trend in the mean SFR is shared by galaxies which are highly star forming, while those which are weakly star forming show no statistically significant correlation between their environment and the mean SFR at any radius.Comment: 37 pages, 11 figures. Referee's comments included and matches version accepted for publication in the Astrophysical Journal. For high resolution figures, see http://www.phyast.pitt.edu/~welikala/pixelz/paper1

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: http://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA