Transient dynamics for sequence processing neural networks: effect of degree distributions

We derive a analytic evolution equation for overlap parameters including the effect of degree distribution on the transient dynamics of sequence processing neural networks. In the special case of globally coupled networks, the precisely retrieved critical loading ratio $\alpha_c = N ^{-1/2}$ is obtained, where $N$ is the network size. In the presence of random networks, our theoretical predictions agree quantitatively with the numerical experiments for delta, binomial, and power-law degree distributions.Comment: 11 pages, 6 figure

Entropy production rates of bistochastic strictly contractive quantum channels on a matrix algebra

We derive, for a bistochastic strictly contractive quantum channel on a matrix algebra, a relation between the contraction rate and the rate of entropy production. We also sketch some applications of our result to the statistical physics of irreversible processes and to quantum information processing.Comment: 7 pages; revised version submitted to J. Phys.

Moving word learning to a novel space: A dynamic systems view of referent selection and retention

Theories of cognitive development must address both the issue of how children bring their knowledge to bear on behavior in-the-moment, and how knowledge changes over time. We argue that seeking answers to these questions requires an appreciation of the dynamic nature of the developing system in its full, reciprocal complexity. We illustrate this dynamic complexity with results from two lines of research on early word learning. The first demonstrates how the child’s active engagement with objects and people supports referent selection via memories for what objects were previously seen in a cued location. The second set of results highlights changes in the role of novelty and attentional processes in referent selection and retention as children’s knowledge of words and objects grows. Together this work suggests understanding systems for perception, action, attention, and memory and their complex interaction is critical to understand word learning. We review recent literature that highlights the complex interactions between these processes in cognitive development and point to critical issues for future work

State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand

Infective endocarditis with Lactococcus garvieae in Japan: a case report

<p>Abstract</p> <p>Introduction</p> <p><it>Lactococcus garvieae </it>is a well-recognized fish pathogen, and it is considered a rare pathogen with low virulence in human infection. We describe the 11th case of <it>L. garvieae </it>infective endocarditis reported in the literature, and the first reported case in Japan.</p> <p>Case presentation</p> <p>We report a case of a 55-year-old Japanese woman who had native valve endocarditis with <it>L. garvieae</it>. The case was complicated by renal infarction, cerebral infarction, and mycotic aneurysms. After anti-microbial treatment, she was discharged from the hospital and is now well while being monitored in the out-patient clinic.</p> <p>Conclusion</p> <p>We encountered a case of <it>L. garvieae </it>endocarditis that occurred in a native valve of a healthy woman. The 16S ribosomal RNA gene sequencing was useful for the identification of this pathogen. Although infective endocarditis with <it>L. garvieae </it>is uncommon, it is possible to treat high virulence clinically.</p

The DSF family of quorum sensing signals: diversity, biosynthesis, and turnover

The diffusible signaling factor (DSF)-based quorum sensing (QS) system has emerged as a widely conserved cell–cell communication mechanism in Gram-negative bacteria. Typically, signals from the DSF family are cis-2-unsaturated fatty acids which regulate diverse biological functions. Recently, substantial progress has been made on the characterization of new members of this family of signals. There have also been new developments in the understanding of the biosynthesis of these molecules where dual enzymatic activities of the DSF synthase and the use of various substrates have been described. The recent discovery of a naturally occurring DSF turnover mechanism and its regulation provides a new dimension in our understanding of how DSF-dependent microorganisms modulate virulence gene expression in response to changes in the surrounding environment

Combining Independent, Weighted P-Values: Achieving Computational Stability by a Systematic Expansion with Controllable Accuracy

Given the expanding availability of scientific data and tools to analyze them, combining different assessments of the same piece of information has become increasingly important for social, biological, and even physical sciences. This task demands, to begin with, a method-independent standard, such as the -value, that can be used to assess the reliability of a piece of information. Good's formula and Fisher's method combine independent -values with respectively unequal and equal weights. Both approaches may be regarded as limiting instances of a general case of combining -values from groups; -values within each group are weighted equally, while weight varies by group. When some of the weights become nearly degenerate, as cautioned by Good, numeric instability occurs in computation of the combined -values. We deal explicitly with this difficulty by deriving a controlled expansion, in powers of differences in inverse weights, that provides both accurate statistics and stable numerics. We illustrate the utility of this systematic approach with a few examples. In addition, we also provide here an alternative derivation for the probability distribution function of the general case and show how the analytic formula obtained reduces to both Good's and Fisher's methods as special cases. A C++ program, which computes the combined -values with equal numerical stability regardless of whether weights are (nearly) degenerate or not, is available for download at our group website http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads/CoinedPValues.html

Pairwise maximum entropy models for studying large biological systems: when they can and when they can't work

One of the most critical problems we face in the study of biological systems is building accurate statistical descriptions of them. This problem has been particularly challenging because biological systems typically contain large numbers of interacting elements, which precludes the use of standard brute force approaches. Recently, though, several groups have reported that there may be an alternate strategy. The reports show that reliable statistical models can be built without knowledge of all the interactions in a system; instead, pairwise interactions can suffice. These findings, however, are based on the analysis of small subsystems. Here we ask whether the observations will generalize to systems of realistic size, that is, whether pairwise models will provide reliable descriptions of true biological systems. Our results show that, in most cases, they will not. The reason is that there is a crossover in the predictive power of pairwise models: If the size of the subsystem is below the crossover point, then the results have no predictive power for large systems. If the size is above the crossover point, the results do have predictive power. This work thus provides a general framework for determining the extent to which pairwise models can be used to predict the behavior of whole biological systems. Applied to neural data, the size of most systems studied so far is below the crossover point