Virus isolation studies suggest short-term variations in abundance in natural cyanophage populations of the Indian Ocean

Cyanophage abundance has been shown to fluctuate over long timescales and with depth, but little is known about how it varies over short timescales. Previous short-term studies have relied on counting total virus numbers and therefore the phages which infect cyanobacteria cannot be distinguished from the total count. In this study, an isolation-based approach was used to determine cyanophage abundance from water samples collected over a depth profile for a 24 h period from the Indian Ocean. Samples were used to infect Synechococcus sp. WH7803 and the number of plaque forming units (pfu) at each time point and depth were counted. At 10 m phage numbers were similar for most time-points, but there was a distinct peak in abundance at 0100 hours. Phage numbers were lower at 25 m and 50 m and did not show such strong temporal variation. No phages were found below this depth. Therefore, we conclude that only the abundance of phages in surface waters showed a clear temporal pattern over a short timescale. Fifty phages from a range of depths and time points were isolated and purified. The molecular diversity of these phages was estimated using a section of the phage-encoded psbD gene and the results from a phylogenetic analysis do not suggest that phages from the deeper waters form a distinct subgroup

Chaotic quantum dots with strongly correlated electrons

Quantum dots pose a problem where one must confront three obstacles: randomness, interactions and finite size. Yet it is this confluence that allows one to make some theoretical advances by invoking three theoretical tools: Random Matrix theory (RMT), the Renormalization Group (RG) and the 1/N expansion. Here the reader is introduced to these techniques and shown how they may be combined to answer a set of questions pertaining to quantum dotsComment: latex file 16 pages 8 figures, to appear in Reviews of Modern Physic

Application of the pressure sensitive paint technique to steady and unsteady flow

Pressure sensitive paint is a newly-developed optical measurement technique with which one can get a continuous pressure distribution in much shorter time and lower cost than a conventional pressure tap measurement. However, most of the current pressure sensitive paint applications are restricted to steady pressure measurement at high speeds because of the small signal-to-noise ratio at low speed and a slow response to pressure changes. In the present study, three phases of work have been completed to extend the application of the pressure sensitive paint technique to low-speed testing and to investigate the applicability of the paint technique to unsteady flow. First the measurement system using a commercially available PtOEP/GP-197 pressure sensitive paint was established and applied to impinging jet measurements. An in-situ calibration using only five pressure tap data points was applied and the results showed good repeatability and good agreement with conventional pressure tap measurements on the whole painted area. The overall measurement accuracy in these experiments was found to be within 0.1 psi. The pressure sensitive paint technique was then applied to low-speed wind tunnel tests using a 60 deg delta wing model with leading edge blowing slots. The technical problems encountered in low-speed testing were resolved by using a high grade CCD camera and applying corrections to improve the measurement accuracy. Even at 35 m/s, the paint data not only agreed well with conventional pressure tap measurements but also clearly showed the suction region generated by the leading edge vortices. The vortex breakdown was also detected at alpha=30 deg. It was found that a pressure difference of 0.2 psi was required for a quantitative pressure measurement in this experiment and that temperature control or a parallel temperature measurement is necessary if thermal uniformity does not hold on the model. Finally, the pressure sensitive paint was applied to a periodically changing pressure field with a 12.8s time period. A simple first-order pole model was applied to deal with the phase lag of the paint. The unsteady pressure estimated from the time-changing pressure sensitive paint data agreed well with the pressure transducer data in regions of higher pressure and showed the possibility of extending the technique to unsteady pressure measurements. However, the model still needs further refinement based on the physics of the oxygen diffusion into the paint layer and the oxygen quenching on the paint luminescence

Random matrix theory within superstatistics

We propose a generalization of the random matrix theory following the basic prescription of the recently suggested concept of superstatistics. Spectral characteristics of systems with mixed regular-chaotic dynamics are expressed as weighted averages of the corresponding quantities in the standard theory assuming that the mean level spacing itself is a stochastic variable. We illustrate the method by calculating the level density, the nearest-neighbor-spacing distributions and the two-level correlation functions for system in transition from order to chaos. The calculated spacing distribution fits the resonance statistics of random binary networks obtained in a recent numerical experiment.Comment: 20 pages, 6 figure

Towards Biologically Plausible Convolutional Networks

Convolutional networks are ubiquitous in deep learning. They are particularly useful for images, as they reduce the number of parameters, reduce training time, and increase accuracy. However, as a model of the brain they are seriously problematic, since they require weight sharing - something real neurons simply cannot do. Consequently, while neurons in the brain can be locally connected (one of the features of convolutional networks), they cannot be convolutional. Locally connected but non-convolutional networks, however, significantly underperform convolutional ones. This is troublesome for studies that use convolutional networks to explain activity in the visual system. Here we study plausible alternatives to weight sharing that aim at the same regularization principle, which is to make each neuron within a pool react similarly to identical inputs. The most natural way to do that is by showing the network multiple translations of the same image, akin to saccades in animal vision. However, this approach requires many translations, and doesn't remove the performance gap. We propose instead to add lateral connectivity to a locally connected network, and allow learning via Hebbian plasticity. This requires the network to pause occasionally for a sleep-like phase of "weight sharing". This method enables locally connected networks to achieve nearly convolutional performance on ImageNet and improves their fit to the ventral stream data, thus supporting convolutional networks as a model of the visual stream

A conjecture on Hubbard-Stratonovich transformations for the Pruisken-Sch\"afer parameterisations of real hyperbolic domains

Rigorous justification of the Hubbard-Stratonovich transformation for the Pruisken-Sch\"afer type of parameterisations of real hyperbolic O(m,n)-invariant domains remains a challenging problem. We show that a naive choice of the volume element invalidates the transformation, and put forward a conjecture about the correct form which ensures the desired structure. The conjecture is supported by complete analytic solution of the problem for groups O(1,1) and O(2,1), and by a method combining analytical calculations with a simple numerical evaluation of a two-dimensional integral in the case of the group O(2,2).Comment: Published versio

Critical Behaviour of the Number of Minima of a Random Landscape at the Glass Transition Point and the Tracy-Widom distribution

We exploit a relation between the mean number $N_{m}$ of minima of random Gaussian surfaces and extreme eigenvalues of random matrices to understand the critical behaviour of $N_{m}$ in the simplest glass-like transition occuring in a toy model of a single particle in $N$-dimensional random environment, with $N\gg 1$. Varying the control parameter $\mu$ through the critical value $\mu_c$ we analyse in detail how $N_{m}(\mu)$ drops from being exponentially large in the glassy phase to $N_{m}(\mu)\sim 1$ on the other side of the transition. We also extract a subleading behaviour of $N_{m}(\mu)$ in both glassy and simple phases. The width $\delta{\mu}/\mu_c$ of the critical region is found to scale as $N^{-1/3}$ and inside that region $N_{m}(\mu)$ converges to a limiting shape expressed in terms of the Tracy-Widom distribution

Spectrophotometric Determination of Os(VIII) Using 2,3-Dihydroxypyridine & 5-Chloropyridine-2,3-diol

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