Comparison of the arrhythmogenicity of acepromazine, xylazine and their combination in pentobarbital-anesthetized rats

Preanesthetic medications are often used in combination with injectable anesthetics in a variety of laboratory animal species. Simultaneous administration of sedative drugs, such as alpha2-adrenergic agonists and phenothiazines, provides muscle relaxation and reduces induction doses of anesthetic agents. However, these drugs may have significant cardiovascular and arrythmogenic effects which may contribute to anesthetic morbidity and mortality (Dyson et al., 1998).Results of previous reports indicate that xylazine, an alpha2-adrenergic agonist, may sensitize the myocardium to epinephrine in dogs anesthetized with halothane (Muir et al., 1975; Tranquilli et al., 1986), isoflurane (Tranquilli et al., 1988) and ketamine (Wright et al., 1987); whereas, acepromazine, a phenothiazine tranquilizer, possessed a protective action against catecholamine-induced arrhythmia in dogs anesthetized with halothane (Muir et al., 1975; Dyson & Pettifer, 1997). The male rat has been used as an animal model to determine the arrhythmic doses of epinephrine during halothane and isoflurane anesthesia (Laster et al., 1990). Rats are commonly used for scientific research and may be anesthetized using injectable or inhalant anesthetic agents for a variety of surgical procedures (Flecknell, 2009); however, injectable anesthetics are commonly preferred in a laboratory setting.Pentobarbital, as a short acting barbiturate anesthetic, is used for short surgical procedures in rats. It is rapidly absorbed following intraperitoneal administration and provide anesthesia for up to 60 min in the rat (Flecknell, 2009).The purpose of this study was to evaluate the effects of clinical doses of acepromazine, xylazine and their combination on the occurrence of epinephrine induced arrhythmia in rats under pentobarbital anesthesia

Collision efficiency of a pollutant particle onto a long cylinder in low Reynolds number fluid flow

A method for calculating the collision efficiency of a small pollutant particle onto a solid long circular cylinder in a low Reynolds number fluid flow with inertia affects is presented. The cylinder is considered at rest in a uniform undisturbed flow at infinity, in the direction perpendicular to the cylinder axis.Assuming that the Reynolds number R based on cylinder radius b is very small but not zero ($R ll 1$), and the Reynolds number Re based on cylinder length l is of order unity, the force per unit length of the cylinder, correct to the order of R, is obtained, first for a general flow direction and then for the case of flow perpendicular to the cylinder axis. This is done by using the Naiver-Stokes equations in long slender bodies theory and applying matched asymptotic expansions in terms of the ratio $kappa$ of radius to body length. Flow field around the cylinder is calculated and the equation of particle motion is developed by applying Newton's second law of motion. The initial particle velocity far from the cylinder is calculated analytically and the particle trajectory course is solved numerically as an initial value problem by using Richardson Extrapolation and the Bulirsch-Stoer method.The collision Efficiency E is obtained by trial and error and is plotted against the dimensionless particle parameter p for different values of R (from 10$sp{-6}$ to 1). The numerical calculations show that the curves have a tendency to move to the right and become like a straight-line as R gets very small. The points at which E is less than 0.005 are also predicted

Parallel Algorithms for Processing Massive Texts and Graphs

Designing parallel algorithms with sublinear space is an inevitable scenario when the input data does not fit in the memory of available systems. In recent years, with the abundance of data and the increasing demand for large scale data processing, the size of datasets easily surpasses that of the typical machine’s memory. Examples of the problem domain vary from social network graphs to DNA sequences. However, to address any problem subject to this restriction efficiently, we need alternative models of computation as the traditional RAM model is no longer an option. The main focus of our research is studying classical algorithms on texts and graphs in the Massively Parallel Computation model. During the last decade, the Massively Parallel Computation (MPC) model attracted a considerable amount of attention. The MPC model was originally proposed to provide a theoretical foundation to algorithms implemented on modern large scale data processing frameworks such as MapReduce, Hadoop, and Spark. The key idea behind this model, and also aforementioned frameworks, is to use many machines to compensate for the shortage of space in individual machines. In this model, the data is distributed among a set of machines each with a sublinear memory, and the process is consisted of several rounds. In each round, machines perform an arbitrary amount of computation on their local data independently. At the end of each round, machines can communicate with each other. We propose constant rounds MPC algorithms for Edge Coloring, Pattern Matching, constructing Suffix Trees, Longest Common Substring, and Longest Palindrome Substring. Recently, the Adaptive Massively Parallel Computation (AMPC) model was introduced as an extension of MPC. Despite the significant progress in designing highly efficient MPC algorithms, the community is facing an impenetrable barrier due to a hardness conjecture that Graph Connectivity requires at least a logarithmic number of rounds. Inspired by this limitation, the AMPC model adds distributed hash-tables to the setup where each machine can adaptively read from them during each round. This tweak not only leads to a constant-round Graph Connectivity algorithm and a plethora of logarithmic improvements in many graph problems, but it is also practical thanks to the recent developments in hardware infrastructure and technologies such as RDMA and eRPC. We explore the limitations of this model by introducing a general Tree Contraction framework which translates well-studied applications in the context of PRAM into constant-round AMPC algorithms, and solving the Minimum Cut problem in sublogarithmic rounds