Associations Between Vaspin Rs2236242 Gene Polymorphism, Walking Time and the Risk of Metabolic Syndrome.

The associations between serum vaspin levels and metabolic or coronary artery disease (CAD) and polycystic ovary syndrome (PCOS) is under the scope of current researchers. Therefore, this adipokine can be considered as a biomarker of metabolic syndrome (MetS). The aim of the study was to analyze the associations between the vaspin rs2236242 polymorphism and physical activity in relation to MetS and its components. The analysis involved the genetic material and clinical data of 108 individuals with MetS and 110 controls. Vaspin rs2236242 polymorphism was detected using the tetra-primer amplification-refractory mutation system polymerase chain reaction (T-ARMS PCR) method. The TA genotype of vaspin rs2236242 was associated with a greater risk of MetS and its components compared with the TT genotype. The analysis of interactions between genotype and walking time revealed that a walking time longer than 60 min./day significantly decreased the risk of MetS in the TA carriers (p = 0.007). The obtained results suggest that any unfavorable effect of the TA genotype of the vaspin rs2236242 polymorphism can be essentially reduced, or even reversed, in a case of individuals walking longer than 60 min. a day. The analysis of the interaction between vaspin rs2236242 polymorphism and walking showed that a walking time of longer than 1 hour a day significantly reduced the risk of MetS, elevated blood pressure and triglycerides concentration.

Predictability, complexity, and learning.

We define predictive information I(pred)(T) as the mutual information between the past and the future of a time series. Three qualitatively different behaviors are found in the limit of large observation times T:I(pred)(T) can remain finite, grow logarithmically, or grow as a fractional power law. If the time series allows us to learn a model with a finite number of parameters, then I(pred)(T) grows logarithmically with a coefficient that counts the dimensionality of the model space. In contrast, power-law growth is associated, for example, with the learning of infinite parameter (or nonparametric) models such as continuous functions with smoothness constraints. There are connections between the predictive information and measures of complexity that have been defined both in learning theory and the analysis of physical systems through statistical mechanics and dynamical systems theory. Furthermore, in the same way that entropy provides the unique measure of available information consistent with some simple and plausible conditions, we argue that the divergent part of I(pred)(T) provides the unique measure for the complexity of dynamics underlying a time series. Finally, we discuss how these ideas may be useful in problems in physics, statistics, and biology.

Neural coding of naturalistic motion stimuli.

We study a wide-field motion-sensitive neuron in the visual system of the blowfly Calliphora vicina. By rotating the fly on a stepper motor outside in a wooded area, and along an angular motion trajectory representative of natural flight, we stimulate the fly's visual system with input that approaches the natural situation. The neural response is analysed in the framework of information theory, using methods that are free from assumptions. We demonstrate that information about the motion trajectory increases as the light level increases over a natural range. This indicates that the fly's brain utilizes the increase in photon flux to extract more information from the photoreceptor array, suggesting that imprecision in neural signals is dominated by photon shot noise in the physical input, rather than by noise generated within the nervous system itself.

Efficiency and ambiguity in an adaptive neural code.

We examine the dynamics of a neural code in the context of stimuli whose statistical properties are themselves evolving dynamically. Adaptation to these statistics occurs over a wide range of timescales-from tens of milliseconds to minutes. Rapid components of adaptation serve to optimize the information that action potentials carry about rapid stimulus variations within the local statistical ensemble, while changes in the rate and statistics of action-potential firing encode information about the ensemble itself, thus resolving potential ambiguities. The speed with which information is optimized and ambiguities are resolved approaches the physical limit imposed by statistical sampling and noise.

Synergy in a neural code.

We show that the information carried by compound events in neural spike trains-patterns of spikes across time or across a population of cells-can be measured, independent of assumptions about what these patterns might represent. By comparing the information carried by a compound pattern with the information carried independently by its parts, we directly measure the synergy among these parts. We illustrate the use of these methods by applying them to experiments on the motion-sensitive neuron H1 of the fly's visual system, where we confirm that two spikes close together in time carry far more than twice the information carried by a single spike. We analyze the sources of this synergy and provide evidence that pairs of spikes close together in time may be especially important patterns in the code of H1.

Adaptive rescaling maximizes information transmission.

Adaptation is a widespread phenomenon in nervous systems, providing flexibility to function under varying external conditions. Here, we relate an adaptive property of a sensory system directly to its function as a carrier of information about input signals. We show that the input/output relation of a sensory system in a dynamic environment changes with the statistical properties of the environment. Specifically, when the dynamic range of inputs changes, the input/output relation rescales so as to match the dynamic range of responses to that of the inputs. We give direct evidence that the scaling of the input/output relation is set to maximize information transmission for each distribution of signals. This adaptive behavior should be particularly useful in dealing with the intermittent statistics of natural signals.

On the application of information theory to neural spike trains.

The nervous system represents time-dependent signals in sequences of discrete action potentials or spikes are identical so that information is carried only in the spike arrival times. We show how to quantify this information, in bits, free from any assumptions about which features of the spike train or input waveform are most important. We apply this approach to the analysis of experiments on a variety of systems, including some where we confront severe sampling problems, and discuss some to the results obtained and hopes for future extensions.

Reproducibility and variability in neural spike trains.

To provide information about dynamic sensory stimuli, the pattern of action potentials in spiking neurons must be variable. To ensure reliability these variations must be related, reproducibly, to the stimulus. For H1, a motion-sensitive neuron in the fly's visual system, constant-velocity motion produces irregular spike firing patterns, and spike counts typically have a variance comparable to the mean, for cells in the mammalian cortex. But more natural, time-dependent input signals yield patterns of spikes that are much more reproducible, both in terms of timing and of counting precision. Variability and reproducibility are quantified with ideas from information theory, and measured spike sequences in H1 carry more than twice the amount of information they would if they followed the variance-mean relation seen with constant inputs. Thus, models that may accurately account for the neural response to static stimuli can significantly underestimate the reliability of signal transfer under more natural conditions.

Adaptation of retinal processing to image contrast and spatial scale.

Owing to the limited dynamic range of a neuron's output, neural circuits are faced with a trade-off between encoding the full range of their inputs and resolving gradations among those inputs. For example, the ambient light level varies daily over more than nine orders of magnitude, whereas the firing rate of optic nerve fibres spans less than two. This discrepancy is alleviated by light adaptation: as the mean intensity increases, the retina becomes proportionately less sensitive. However, image statistics other than the mean intensity also vary drastically during routine visual processing. Theory predicts that an efficient visual encoder should adapt its strategy not only to the mean, but to the full shape of the intensity distribution. Here we report that retinal ganglion cells, the output neurons of the retina, adapt to both image contrast-the range of light intensities-and to spatial correlations within the scene, even at constant mean intensity. The adaptation occurs on a scale of seconds, one hundred times more slowly than the immediate light response, and involves 2-5-fold changes in the firing rate. It is mediated within the retinal network: two independent sites of modulation after the photoreceptor cells appear to be involved. Our results demonstrate a remarkable plasticity in retinal processing that may contribute to the contrast adaptation of human vision.

Naturalistic stimuli increase the rate and efficiency of information transmission by primary auditory afferents.

Natural sounds, especially communication sounds, have highly structured amplitude and phase spectra. We have quantified how structure in the amplitude spectrum of natural sounds affects coding in primary auditory afferents. Auditory afferents encode stimuli with naturalistic amplitude spectra dramatically better than broad-band stimuli (approximating white noise); the rate at which the spike train carries information about the stimulus is 2-6 times higher for naturalistic sounds. Furthermore, the information rates can reach 90% of the fundamental limit to information transmission set by the statistics of the spike response. These results indicate that the coding strategy of the auditory nerve is matched to the structure of natural sounds; this 'tuning' allows afferent spike trains to provide higher processing centres with a more complete description of the sensory world.

Reliability and information transmission in spiking neurons.

Spiking neurons encode continuous, time-varying signals in sequences of identical action potentials. Relatively simple algorithms allow one to 'decode' this neural representation of sensory data to estimate the input signals. Decoding experiments provide a quantitative characterization of information transmission and computational reliability under real-time conditions. The results of these studies show that neural coding and computation in several systems approach fundamental physical and informational theoretic limits to performance.

Vibrationally enhanced tunneling as a mechanism for enzymatic hydrogen transfer.

We present a theory of enzymatic hydrogen transfer in which hydrogen tunneling is mediated by thermal fluctuations of the enzyme's active site. These fluctuations greatly increase the tunneling rate by shortening the distance the hydrogen must tunnel. The average tunneling distance is shown to decrease when heavier isotopes are substituted for the hydrogen or when the temperature is increased, leading to kinetic isotope effects (KIEs)--defined as the factor by which the reaction slows down when isotopically substituted substrates are used--that need be no larger than KIEs for nontunneling mechanisms. Within this theory we derive a simple KIE expression for vibrationally enhanced ground state tunneling that is able to fit the data for the bovine serum amine oxidase (BSAO) system, correctly predicting the large temperature dependence of the KIEs. Because the KIEs in this theory can resemble those for nontunneling dynamics, distinguishing the two possibilities requires careful measurements over a range of temperatures, as has been done for BSAO.

Virtual intermediates in photosynthetic electron transfer.

We explore the possibility of virtual transfer in the primary charge separation of photosynthetic bacteria within the context of several types of experimental data. We show that the peak that might be expected in the virtual rate as electric fields vary the intermediate state energy is severely broadened by coupling to high-frequency modes. The Stark absorption kinetics data are thus consistent with virtual transfer in the primary charge separation. High-frequency coupling also makes the temperature dependence weak over a wide range of parameters. We demonstrate that Stark fluorescence anisotropy data, usually taken as evidence of virtual transfer, can in fact be consistent with two-step transfer. We suggest a two-pulse excitation experiment to quantify the contributions from two-step and virtual transfer. We show that virtual absorption into a charge transfer state can make a substantial contribution to the Stark absorption spectrum in a way that is not related to any derivative of the absorption spectrum.

Reading a neural code.

Traditional approaches to neural coding characterize the encoding of known stimuli in average neural responses. Organisms face nearly the opposite task--extracting information about an unknown time-dependent stimulus from short segments of a spike train. Here the neural code was characterized from the point of view of the organism, culminating in algorithms for real-time stimulus estimation based on a single example of the spike train. These methods were applied to an identified movement-sensitive neuron in the fly visual system. Such decoding experiments determined the effective noise level and fault tolerance of neural computation, and the structure of the decoding algorithms suggested a simple model for real-time analog signal processing with spiking neurons.

Temporal filtering in retinal bipolar cells. Elements of an optimal computation?

Recent experiments indicate that the dark-adapted vertebrate visual system can count photons with a reliability limited by dark noise in the rod photoreceptors themselves. This suggests that subsequent layers of the retina, responsible for signal processing, add little if any excess noise and extract all the available information. Given the signal and noise characteristics of the photoreceptors, what is the structure of such an optimal processor? We show that optimal estimates of time-varying light intensity can be accomplished by a two-stage filter, and we suggest that the first stage should be identified with the filtering which occurs at the first anatomical stage in retinal signal processing, signal transfer from the rod photoreceptor to the bipolar cell. This leads to parameter-free predictions of the bipolar cell response, which are in excellent agreement with experiments comparing rod and bipolar cell dynamics in the same retina. As far as we know this is the first case in which the computationally significant dynamics of a neuron could be predicted rather than modeled.