Electrical colonic stimulation reduces mean transit time in a porcine model.

Abstract Electrical stimulation is a new way to treat digestive disorders such as constipation. Colonic propulsive activity can be triggered by battery operated devices. This study aimed to demonstrate the effect of direct electrical colonic stimulation on mean transit time in a chronic porcine model. The impact of stimulation and implanted material on the colonic wall was also assessed. Three pairs of electrodes were implanted into the caecal wall of 12 anaesthetized pigs. Reference colonic transit time was determined by radiopaque markers for each pig before implantation. It was repeated 4 weeks after implantation with sham stimulation and 5 weeks after implantation with electrical stimulation. Aboral sequential trains of 1-ms pulse width (10 V; 120 Hz) were applied twice daily for 6 days, using an external battery operated stimulator. For each course of markers, a mean value was computed from transit times obtained from individual pig. Microscopic examination of the caecum was routinely performed after animal sacrifice. A reduction of mean transit time was observed after electrical stimulation (19 +/- 13 h; mean +/- SD) when compared to reference (34 +/- 7 h; P = 0.045) and mean transit time after sham stimulation (36 +/- 9 h; P = 0.035). Histological examination revealed minimal chronic inflammation around the electrodes. Colonic transit time measured in a chronic porcine model is reduced by direct sequential electrical stimulation. Minimal tissue lesion is elicited by stimulation or implanted material. Electrical colonic stimulation could be a promising approach to treat specific disorders of the large bowel.

Exact solution to the mean exit time problem for free inertial processes driven by Gaussian white noise

We obtain the exact analytical expression, up to a quadrature, for the mean exit time, T(x,v), of a free inertial process driven by Gaussian white noise from a region (0,L) in space. We obtain a completely explicit expression for T(x,0) and discuss the dependence of T(x,v) as a function of the size L of the region. We develop a new method that may be used to solve other exit time problems.

Scaling and data collapse for the mean exit time of asset prices

We study theoretical and empirical aspects of the mean exit time (MET) of financial time series. The theoretical modeling is done within the framework of continuous time random walk. We empirically verify that the mean exit time follows a quadratic scaling law and it has associated a prefactor which is specific to the analyzed stock. We perform a series of statistical tests to determine which kind of correlation are responsible for this specificity. The main contribution is associated with the autocorrelation property of stock returns. We introduce and solve analytically both two-state and three-state Markov chain models. The analytical results obtained with the two-state Markov chain model allows us to obtain a data collapse of the 20 measured MET profiles in a single master curve.

Mean first-passage times for systems driven by gamma and McFadden dichotomous noise

We consider mean first-passage times (MFPTs) for systems driven by non-Markov gamma and McFadden dichotomous noises. A simplified derivation is given of the underlying integral equations and the theory for ordinary renewal processes is extended to modified and equilibrium renewal processes. The exact results are compared with the MFPT for Markov dichotomous noise and with the results of Monte Carlo simulations.

Mean first-passage times for systems driven by equilibrium persistent-periodic dichotomous noise

In a recent paper, [J. M. Porrà, J. Masoliver, and K. Lindenberg, Phys. Rev. E 48, 951 (1993)], we derived the equations for the mean first-passage time for systems driven by the coin-toss square wave, a particular type of dichotomous noisy signal, to reach either one of two boundaries. The coin-toss square wave, which we here call periodic-persistent dichotomous noise, is a random signal that can only change its value at specified time points, where it changes its value with probability q or retains its previous value with probability p=1-q. These time points occur periodically at time intervals t. Here we consider the stationary version of this signal, that is, equilibrium periodic-persistent noise. We show that the mean first-passage time for systems driven by this stationary noise does not show either the discontinuities or the oscillations found in the case of nonstationary noise. We also discuss the existence of discontinuities in the mean first-passage time for random one-dimensional stochastic maps.

Mean first-passage time for system driven by the coin-toss square wave

We study the mean-first-passage-time problem for systems driven by the coin-toss square-wave signal. Exact analytic solutions are obtained for the driftless case. We also obtain approximate solutions for the potential case. The mean-first-passage time exhibits discontinuities and a remarkable nonsmooth oscillatory behavior which, to our knowledge, has not been observed for other kinds of driving noise.

Quantum critical effects in mean-field glassy systems

We consider the effects of quantum fluctuations in mean-field quantum spin-glass models with pairwise interactions. We examine the nature of the quantum glass transition at zero temperature in a transverse field. In models (such as the random orthogonal model) where the classical phase transition is discontinuous an analysis using the static approximation reveals that the transition becomes continuous at zero temperature.

Evidence of aging in spin glass mean-field models

We study numerically the out-of-equilibrium dynamics of the hypercubic cell spin glass in high dimensionalities. We obtain evidence of aging effects qualitatively similar both to experiments and to simulations of low-dimensional models. This suggests that the Sherrington-Kirkpatrick model as well as other mean-field finite connectivity lattices can be used to study these effects analytically.

The mean-variance model from the inverse of the variance-covariance matrix

En este artículo, a partir de la inversa de la matriz de varianzas y covarianzas se obtiene el modelo Esperanza-Varianza de Markowitz siguiendo un camino más corto y matemáticamente riguroso. También se obtiene la ecuación de equilibrio del CAPM de Sharpe.

Mean first-passage time of continuous non-Markovian processes driven by colored noise

An equation for mean first-passage times of non-Markovian processes driven by colored noise is derived through an appropriate backward integro-differential equation. The equation is solved in a Bourret-like approximation. In a weak-noise bistable situation, non-Markovian effects are taken into account by an effective diffusion coefficient. In this situation, our results compare satisfactorily with other approaches and experimental data.

Alcohol drinking, the metabolic syndrome and diabetes in a population with high mean alcohol consumption.

AIMS: To investigate the relationship of alcohol consumption with the metabolic syndrome and diabetes in a population-based study with high mean alcohol consumption. Few data exist on these conditions in high-risk drinkers. METHODS: In 6172 adults aged 35-75 years, alcohol consumption was categorized as 0, 1-6, 7-13, 14-20, 21-27, 28-34 and ≥ 35 drinks/week or as non-drinkers (0), low-risk (1-13), medium-to-high-risk (14-34) and very-high-risk (≥ 35) drinkers. Alcohol consumption was objectively confirmed by biochemical tests. In multivariate analysis, we assessed the relationship of alcohol consumption with adjusted prevalence of the metabolic syndrome, diabetes and insulin resistance, determined with the homeostasis model assessment of insulin resistance (HOMA-IR). RESULTS: Seventy-three per cent of participants consumed alcohol, 16% were medium-to-high-risk drinkers and 2% very-high-risk drinkers. In multivariate analysis, the prevalence of the metabolic syndrome, diabetes and mean HOMA-IR decreased with low-risk drinking and increased with high-risk drinking. Adjusted prevalence of the metabolic syndrome was 24% in non-drinkers, 19% in low-risk (P<0.001 vs. non-drinkers), 20% in medium-to-high-risk and 29% in very-high-risk drinkers (P=0.005 vs. low-risk). Adjusted prevalence of diabetes was 6.0% in non-drinkers, 3.6% in low-risk (P<0.001 vs. non-drinkers), 3.8% in medium-to-high-risk and 6.7% in very-high-risk drinkers (P=0.046 vs. low-risk). Adjusted HOMA-IR was 2.47 in non-drinkers, 2.14 in low-risk (P<0.001 vs. non-drinkers), 2.27 in medium-to-high-risk and 2.53 in very-high-risk drinkers (P=0.04 vs. low-risk). These relationships did not differ according to beverage types. CONCLUSIONS: Alcohol has a U-shaped relationship with the metabolic syndrome, diabetes and HOMA-IR, without differences between beverage types.

A comparison of mean phase difference and generalized least squares for analyzing single-case data

The present study focuses on single-case data analysis and specifically on two procedures for quantifying differences between baseline and treatment measurements The first technique tested is based on generalized least squares regression analysis and is compared to a proposed non-regression technique, which allows obtaining similar information. The comparison is carried out in the context of generated data representing a variety of patterns (i.e., independent measurements, different serial dependence underlying processes, constant or phase-specific autocorrelation and data variability, different types of trend, and slope and level change). The results suggest that the two techniques perform adequately for a wide range of conditions and researchers can use both of them with certain guarantees. The regression-based procedure offers more efficient estimates, whereas the proposed non-regression procedure is more sensitive to intervention effects. Considering current and previous findings, some tentative recommendations are offered to applied researchers in order to help choosing among the plurality of single-case data analysis techniques.

Generation mean analysis in relation to polygenic systems with epistasis and fixed genes

Epistatic effects involving genic combinations of fixed and non fixed genes are shown to contribute to the genotypic mean of any population. These effects define specific additive x additive and additive x dominant epistatic components. As such components are not estimable, their relative importance cannot be assessed. These epistatic effects can cause bias in the estimates of the additive and dominance components to which they are confounded. The magnitude of the bias depends on the relative values of the epistatic effects, comparatively to deviations d and h, type of prevailing epistasis and direction of dominance.

In vitro activity of gallium maltolate against Staphylococci in logarithmic, stationary, and biofilm growth phases: comparison of conventional and calorimetric susceptibility testing methods.

Ga(3+) is a semimetal element that competes for the iron-binding sites of transporters and enzymes. We investigated the activity of gallium maltolate (GaM), an organic gallium salt with high solubility, against laboratory and clinical strains of methicillin-susceptible Staphylococcus aureus (MSSA), methicillin-resistant S. aureus (MRSA), methicillin-susceptible Staphylococcus epidermidis (MSSE), and methicillin-resistant S. epidermidis (MRSE) in logarithmic or stationary phase and in biofilms. The MICs of GaM were higher for S. aureus (375 to 2000 microg/ml) than S. epidermidis (94 to 200 microg/ml). Minimal biofilm inhibitory concentrations were 3,000 to >or=6,000 microg/ml (S. aureus) and 94 to 3,000 microg/ml (S. epidermidis). In time-kill studies, GaM exhibited a slow and dose-dependent killing, with maximal action at 24 h against S. aureus of 1.9 log(10) CFU/ml (MSSA) and 3.3 log(10) CFU/ml (MRSA) at 3x MIC and 2.9 log(10) CFU/ml (MSSE) and 4.0 log(10) CFU/ml (MRSE) against S. epidermidis at 10x MIC. In calorimetric studies, growth-related heat production was inhibited by GaM at subinhibitory concentrations; and the minimal heat inhibition concentrations were 188 to 4,500 microg/ml (MSSA), 94 to 1,500 microg/ml (MRSA), and 94 to 375 microg/ml (MSSE and MRSE), which correlated well with the MICs. Thus, calorimetry was a fast, accurate, and simple method useful for investigation of antimicrobial activity at subinhibitory concentrations. In conclusion, GaM exhibited activity against staphylococci in different growth phases, including in stationary phase and biofilms, but high concentrations were required. These data support the potential topical use of GaM, including its use for the treatment of wound infections, MRSA decolonization, and coating of implants.