Origin of Secretin Receptor Precedes the Advent of Tetrapoda: Evidence on the Separated Origins of Secretin and Orexin

At present, secretin and its receptor have only been identified in mammals, and the origin of this ligand-receptor pair in early vertebrates is unclear. In addition, the elusive similarities of secretin and orexin in terms of both structures and functions suggest a common ancestral origin early in the vertebrate lineage. In this article, with the cloning and functional characterization of secretin receptors from lungfish and X. laevis as well as frog (X. laevis and Rana rugulosa) secretins, we provide evidence that the secretin ligand-receptor pair has already diverged and become highly specific by the emergence of tetrapods. The secretin receptor-like sequence cloned from lungfish indicates that the secretin receptor was descended from a VPAC-like receptor prior the advent of sarcopterygians. To clarify the controversial relationship of secretin and orexin, orexin type-2 receptor was cloned from X. laevis. We demonstrated that, in frog, secretin and orexin could activate their mutual receptors, indicating their coordinated complementary role in mediating physiological processes in non-mammalian vertebrates. However, among the peptides in the secretin/glucagon superfamily, secretin was found to be the only peptide that could activate the orexin receptor. We therefore hypothesize that secretin and orexin are of different ancestral origins early in the vertebrate lineage

Parameterizing Spatial Models of Infectious Disease Transmission that Incorporate Infection Time Uncertainty Using Sampling-Based Likelihood Approximations.

A class of discrete-time models of infectious disease spread, referred to as individual-level models (ILMs), are typically fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. These models quantify probabilistic outcomes regarding the risk of infection of susceptible individuals due to various susceptibility and transmissibility factors, including their spatial distance from infectious individuals. The infectious pressure from infected individuals exerted on susceptible individuals is intrinsic to these ILMs. Unfortunately, quantifying this infectious pressure for data sets containing many individuals can be computationally burdensome, leading to a time-consuming likelihood calculation and, thus, computationally prohibitive MCMC-based analysis. This problem worsens when using data augmentation to allow for uncertainty in infection times. In this paper, we develop sampling methods that can be used to calculate a fast, approximate likelihood when fitting such disease models. A simple random sampling approach is initially considered followed by various spatially-stratified schemes. We test and compare the performance of our methods with both simulated data and data from the 2001 foot-and-mouth disease (FMD) epidemic in the U.K. Our results indicate that substantial computation savings can be obtained--albeit, of course, with some information loss--suggesting that such techniques may be of use in the analysis of very large epidemic data sets

Posterior results for FMD-ILM using the spatial stratification method.

<p>Posterior means and 95% credible intervals for all parameters of the data augmented FMD-ILM under the spatial stratification method. We sampled <i>ρ</i> = 0.50 from each stratum. The results are compared to the full model.</p

Posterior results for FMD-ILM using the SRS method.

<p>Posterior means and 95% credible intervals for all parameters of the data augmented FMD-ILM under the SRS method. The results are compared to the full model to assess accuracy.</p

Posterior results for full MCMC and SRS methods.

<p>Posterior means and 95% credible intervals for <i>α</i>, <i>β</i>, and <i>λ</i>_<i>z</i> for the full MCMC and SRS methods for 10 different epidemics simulated from the data augmented spatial ILM with varying sampling proportions. The dashed, horizontal lines represent the true parameter values: <i>α</i> = 1.4, <i>β</i> = 2.3, and <math><mrow><msub><mo>λ</mo><mi>z</mi></msub><mo>=</mo><mn>1</mn><mn>3</mn></mrow></math>, with a population of size <i>n</i> = 625.</p

Full posterior results for full MCMC and spatial stratification methods.

<p>Posterior means and 95% credible intervals for <i>α</i>, <i>β</i>, and <i>λ</i>_<i>z</i> for the full MCMC and spatial stratification methods for 10 different epidemics simulated from the data augmented spatial ILM with varying values for <i>m</i> and <i>ρ</i>. The dashed, horizontal lines represent the true parameter values: <i>α</i> = 1.4, <i>β</i> = 2.3, and <math><mrow><msub><mo>λ</mo><mi>z</mi></msub><mo>=</mo><mn>1</mn><mn>3</mn></mrow></math>, with a population of size <i>n</i> = 625.</p

Average infectious period under the simulation study.

<p>Illustration of the average infectious period under the simulation study. The average incubation period is 3 days, and the average delay to disease recovery and removal from the population is 4 days. The ‘S’ symbol indicates the individual is susceptible to the disease at that time point and the ‘R’ symbol indicates the individual has recovered from the disease and has been removed from the population at that time point.</p

Workload of horses on a water treadmill: effect of speed and water height on oxygen consumption and cardiorespiratory parameters

Abstract Background Despite the use of water treadmills (WT) in conditioning horses, the intensity of WT exercise has not been well documented. The workload on a WT is a function of water height and treadmill speed. Therefore, the purpose of this study was to determine the effects of these factors on workload during WT exercise. Fifteen client-owned Quarter Horses were used in a randomized, controlled study. Three belt speeds and three water heights (mid cannon, carpus and stifle), along with the control condition (dry treadmill, all three speeds), were tested. Measured outcomes were oxygen consumption (V̇O2), ventilation (respiratory frequency, tidal volume (VT)), heart rate (HR), and blood lactate. An ergospirometry system was used to measure V̇O2 and ventilation. Linear mixed effects models were used to examine the effects of presence or absence of water, water height and speed (as fixed effects) on measured outcomes. Results Water height and its interaction with speed had a significant effect on V̇O2, VT and HR, all peaking at the highest water level and speed (stifle at 1.39 m/s, median V̇O2 = 16.70 ml/(kg.min), VT = 6 L, HR = 69 bpm). Respiratory frequency peaked with water at the carpus at 1.39 m/s (median 49 breaths/min). For a given water height, the small increments in speed did not affect the measured outcomes. Post-exercise blood lactate concentration did not change. Conclusions Varying water height and speed affects the workload associated with WT exercise. The conditions utilized in this study were associated with low intensity exercise. Water height had a greater impact on exercise intensity than speed