Bacteria classification using Cyranose 320 electronic nose

Background An electronic nose (e-nose), the Cyrano Sciences' Cyranose 320, comprising an array of thirty-two polymer carbon black composite sensors has been used to identify six species of bacteria responsible for eye infections when present at a range of concentrations in saline solutions. Readings were taken from the headspace of the samples by manually introducing the portable e-nose system into a sterile glass containing a fixed volume of bacteria in suspension. Gathered data were a very complex mixture of different chemical compounds. Method Linear Principal Component Analysis (PCA) method was able to classify four classes of bacteria out of six classes though in reality other two classes were not better evident from PCA analysis and we got 74% classification accuracy from PCA. An innovative data clustering approach was investigated for these bacteria data by combining the 3-dimensional scatter plot, Fuzzy C Means (FCM) and Self Organizing Map (SOM) network. Using these three data clustering algorithms simultaneously better 'classification' of six eye bacteria classes were represented. Then three supervised classifiers, namely Multi Layer Perceptron (MLP), Probabilistic Neural network (PNN) and Radial basis function network (RBF), were used to classify the six bacteria classes. Results A [6 × 1] SOM network gave 96% accuracy for bacteria classification which was best accuracy. A comparative evaluation of the classifiers was conducted for this application. The best results suggest that we are able to predict six classes of bacteria with up to 98% accuracy with the application of the RBF network. Conclusion This type of bacteria data analysis and feature extraction is very difficult. But we can conclude that this combined use of three nonlinear methods can solve the feature extraction problem with very complex data and enhance the performance of Cyranose 320

Money and mental wellbeing : a longitudinal study of medium-sized lottery wins

One of the famous questions in social science is whether money makes people happy. We offer new evidence by using longitudinal data on a random sample of Britons who receive medium-sized lottery wins of between £1000 and £120,000 (that is, up to approximately US$ 200,000). When compared to two control groups – one with no wins and the other with small wins – these individuals go on eventually to exhibit significantly better psychological health. Two years after a lottery win, the average measured improvement in mental wellbeing is 1.4 GHQ points

Population stability: regulating size in the presence of an adversary

We introduce a new coordination problem in distributed computing that we call the population stability problem. A system of agents each with limited memory and communication, as well as the ability to replicate and self-destruct, is subjected to attacks by a worst-case adversary that can at a bounded rate (1) delete agents chosen arbitrarily and (2) insert additional agents with arbitrary initial state into the system. The goal is perpetually to maintain a population whose size is within a constant factor of the target size $N$. The problem is inspired by the ability of complex biological systems composed of a multitude of memory-limited individual cells to maintain a stable population size in an adverse environment. Such biological mechanisms allow organisms to heal after trauma or to recover from excessive cell proliferation caused by inflammation, disease, or normal development. We present a population stability protocol in a communication model that is a synchronous variant of the population model of Angluin et al. In each round, pairs of agents selected at random meet and exchange messages, where at least a constant fraction of agents is matched in each round. Our protocol uses three-bit messages and $\omega(\log^2 N)$ states per agent. We emphasize that our protocol can handle an adversary that can both insert and delete agents, a setting in which existing approximate counting techniques do not seem to apply. The protocol relies on a novel coloring strategy in which the population size is encoded in the variance of the distribution of colors. Individual agents can locally obtain a weak estimate of the population size by sampling from the distribution, and make individual decisions that robustly maintain a stable global population size

Qualitative Assessment of Gene Expression in Affymetrix Genechip Arrays

Affymetrix Genechip microarrays are used widely to determine the simultaneous expression of genes in a given biological paradigm. Probes on the Genechip array are atomic entities which by definition are randomly distributed across the array and in turn govern the gene expression. In the present study, we make several interesting observations. We show that there is considerable correlation between the probe intensities across the array which defy the independence assumption. While the mechanism behind such correlations is unclear, we show that scaling behavior and the profiles of perfect match (PM) as well as mismatch (MM) probes are similar and immune to background subtraction. We believe that the observed correlations are possibly an outcome of inherent non-stationarities or patchiness in the array devoid of biological significance. This is demonstrated by inspecting their scaling behavior and profiles of the PM and MM probe intensities obtained from publicly available Genechip arrays from three eukaryotic genomes, namely: Drosophila Melanogaster, Homo Sapiens and Mus musculus across distinct biological paradigms and across laboratories, with and without background subtraction. The fluctuation functions were estimated using detrended fluctuation analysis (DFA) with fourth order polynomial detrending. The results presented in this study provide new insights into correlation signatures of PM and MM probe intensities and suggests the choice of DFA as a tool for qualitative assessment of Affymetrix Genechip microarrays prior to their analysis. A more detailed investigation is necessary in order to understand the source of these correlations.Comment: 22 Pages, 7 Figures, 1 Tabl

Dark matter halo occupation: environment and clustering

We use a large dark matter simulation of a LambdaCDM model to investigate the clustering and environmental dependence of the number of substructures in a halo. Focusing on redshift z=1, we find that the halo occupation distribution is sensitive at the tens of percent level to the surrounding density and to a lesser extent to asymmetry of the surrounding density distribution. We compute the autocorrelation function of halos as a function of occupation, building on the finding of Wechsler et al. (2006) and Gao and White (2007) that halos (at fixed mass) with more substructure are more clustered. We compute the relative bias as a function of occupation number at fixed mass, finding a strong relationship. At fixed mass, halos in the top 5% of occupation can have an autocorrelation function ~ 1.5-2 times higher than the mean. We also compute the bias as a function of halo mass, for fixed halo occupation. We find that for group and cluster sized halos, when the number of subhalos is held fixed, there is a strong anticorrelation between bias and halo mass. Such a relationship represents an additional challenge to the halo model.Comment: 13 pages, 10 figures, MNRAS submitte

Detecting neutral hydrogen in emission at redshift z ~ 1

We use a large N-body simulation to examine the detectability of HI in emission at redshift z ~ 1, and the constraints imposed by current observations on the neutral hydrogen mass function of galaxies at this epoch. We consider three different models for populating dark matter halos with HI, designed to encompass uncertainties at this redshift. These models are consistent with recent observations of the detection of HI in emission at z ~ 0.8. Whilst detection of 21 cm emission from individual halos requires extremely long integrations with existing radio interferometers, such as the Giant Meter Radio Telescope (GMRT), we show that the stacked 21 cm signal from a large number of halos can be easily detected. However, the stacking procedure requires accurate redshifts of galaxies. We show that radio observations of the field of the DEEP2 spectroscopic galaxy redshift survey should allow detection of the HI mass function at the 5-12 sigma level in the mass range 10^(11.4) M_sun/h < M_halo < 10^(12.5)M_sun/h, with a moderate amount of observation time. Assuming a larger noise level that corresponds to an upper bound for the expected noise for the GMRT, the detection significance for the HI mass function is still at the 1.7-3 sigma level. We find that optically undetected satellite galaxies enhance the HI emission profile of the parent halo, leading to broader wings as well as a higher peak signal in the stacked profile of a large number of halos. We show that it is in principle possible to discern the contribution of undetected satellites to the total HI signal, even though cosmic variance limitation make this challenging for some of our models.Comment: 14 pages, 9 figures, Submitted To MNRA

A Fresh Look into the Neutron EDM and Magnetic Susceptibility

We reexamine the estimate of the neutron Electric Dipole Moment (NEDM) from chiral and QCD spectral sum rules (QSSR) approaches. In the former, we evaluate the pion mass corrections which are about 5% of the leading Log. results. However, the chiral estimate can be affected by the unknown value of the renormalizaton scale nu. For QSSR, we analyze the effect of the nucleon interpolating currents on the existing predictions. We conclude that previous QSSR results are not obtained within the optimal choice of these operators, which lead to an overestimate of these results by about a factor 4. The weakest upper bound |theta|< 2 10^-9 for the strong CP-violating angle is obtained from QSSR, while the strongest upper bound |theta|< 1.3 10^-10 comes from the chiral approach evaluated at the scale \nu=M_N. We also re-estimate the proton magnetic susceptibility, which is an important input in the QSSR estimate of the NEDM.Comment: Version to appear in Phys. Lett.

Obesity, unhappiness, and the challenge of affluence : theory and evidence

Is affluence a good thing? The book "The Challenge of Affluence" by Avner Offer (2006) argues that economic prosperity weakens self-control and undermines human well-being. Consistent with a pessimistic view, we show that psychological distress has been rising through time in modern Great Britain. Taking over-eating as an example, our data reveal that half the British population view themselves as overweight, and that happiness and mental health are worse among fatter people in Britain and Germany. Comparisons also matter. We discuss problems of inference and argue that longitudinal data are needed. We suggest a theory of obesity imitation where utility depends on relative weight

SIRENE: Supervised Inference of Regulatory Networks

Living cells are the product of gene expression programs that involve the regulated transcription of thousands of genes. The elucidation of transcriptional regulatory networks in thus needed to understand the cell's working mechanism, and can for example be useful for the discovery of novel therapeutic targets. Although several methods have been proposed to infer gene regulatory networks from gene expression data, a recent comparison on a large-scale benchmark experiment revealed that most current methods only predict a limited number of known regulations at a reasonable precision level. We propose SIRENE, a new method for the inference of gene regulatory networks from a compendium of expression data. The method decomposes the problem of gene regulatory network inference into a large number of local binary classification problems, that focus on separating target genes from non-targets for each TF. SIRENE is thus conceptually simple and computationally efficient. We test it on a benchmark experiment aimed at predicting regulations in E. coli, and show that it retrieves of the order of 6 times more known regulations than other state-of-the-art inference methods