The effect of region of application on absorption of ethanol and hexanol through canine skin

The effect of region of application on the percutaneous penetration of solutes with differing lipophilicity was investigated in canine skin. Skin from the thorax, neck, back, groin, and axilla regions was harvested from Greyhound dogs and placed in Franz-type diffusion cells. Radiolabelled (C-14) ethanol (Log P 0.19) or hexanol (Log P 1.94) was applied to each skin section for a total of 5 h. The permeability coefficient (k(P), cm h(-1)) and residue of alcohol remaining in the skin were significantly (P = 0.001) higher for hexanol compared to ethanol. In contrast, ethanol had a far greater maximum flux (J(max), mol (cm(2))(-1) h(-1)) than hexanol (P = 0.001). A comparison of regional differences shows the k(P) and Jmax for ethanol in the groin was significantly lower (P = 0.035) than the back. The k(P) and Jmax for hexanol were significantly higher (P = 0.001) in the axilla than the other four skin sites. An understanding of factors influencing percutaneous drug movement is important when formulating topical preparations for the dog. (C) 2003 Elsevier Ltd. All rights reserved.

Canine model for investigating the impact of oral enrofloxacin on commensal coliforms and colonization with multidrug-resistant Escherichia coli

A model was developed in dogs to determine the impact of oral enrofloxacin administration on the indigenous coliform population in the gastrointestinal tract and subsequent disposition to colonization by a strain of multidrug-resistant Escherichia coli (MDREC). Dogs given a daily oral dose of 5 mg enrofloxacin kg(-1) for 21 consecutive days showed a significant decline in faecal coliforms to levels below detectable limits by 72 In of administration. Subsequently, faecal coliforms remained suppressed throughout the period of enrofloxacin dosing. Upon termination of antibiotic administration, the number of excreted faecal coliforms slowly returned over an 8-day period, to levels comparable to those seen prior to antibiotic treatment. Enrofloxacin-treated dogs were more effectively colonized by MDREC, evidenced by a significantly increased count of MDREC in the faeces (7.1 +/- 1.5 log(10) g(-1)) compared with non-antibiotic-treated dogs (5.2 +/- 1.2; P = 0.003). Furthermore, antibiotic treatment also sustained a significantly longer period of MDREC excretion in the faeces (26.8 +/- 10.5 days) compared with animals not treated with enrofloxacin (8.5 +/- 5.4 days; P = 0.0215). These results confirm the importance of sustained delivery of an antimicrobial agent to maintain and expand the colonization potential of drug-resistant bacteria in vivo, achieved in part by reducing the competing commensal coliforms in the gastrointestinal tract to below detectable levels in the faeces. Without in vivo antimicrobial selection pressure, commensal coliforms dominated the gastrointestinal tract at the expense of the MDREC population. Conceivably, the model developed could be used to test the efficacy of novel non-antibiotic strategies aimed at monitoring and controlling gastrointestinal colonization by multidrug-resistant members of the Enterobacteriaceae that cause nosocomial infections.

The v-MFG test: Investigating maternal, offspring and maternal-fetal genetic incompatibility effects on disease and viability

The MFG test is a family-based association test that detects genetic effects contributing to disease in offspring, including offspring allelic effects, maternal allelic effects and MFG incompatibility effects. Like many other family-based association tests, it assumes that the offspring survival and the offspring-parent genotypes are conditionally independent provided the offspring is affected. However, when the putative disease-increasing locus can affect another competing phenotype, for example, offspring viability, the conditional independence assumption fails and these tests could lead to incorrect conclusions regarding the role of the gene in disease. We propose the v-MFG test to adjust for the genetic effects on one phenotype, e.g., viability, when testing the effects of that locus on another phenotype, e.g., disease. Using genotype data from nuclear families containing parents and at least one affected offspring, the v-MFG test models the distribution of family genotypes conditional on offspring phenotypes. It simultaneously estimates genetic effects on two phenotypes, viability and disease. Simulations show that the v-MFG test produces accurate genetic effect estimates on disease as well as on viability under several different scenarios. It generates accurate type-I error rates and provides adequate power with moderate sample sizes to detect genetic effects on disease risk when viability is reduced. We demonstrate the v-MFG test with HLA-DRB1 data from study participants with rheumatoid arthritis (RA) and their parents, we show that the v-MFG test successfully detects an MFG incompatibility effect on RA while simultaneously adjusting for a possible viability loss.

Space efficient quantile summary for constrained sliding windows on a data stream

In many online applications, we need to maintain quantile statistics for a sliding window on a data stream. The sliding windows in natural form are defined as the most recent N data items. In this paper, we study the problem of estimating quantiles over other types of sliding windows. We present a uniform framework to process quantile queries for time constrained and filter based sliding windows. Our algorithm makes one pass on the data stream and maintains an E-approximate summary. It uses O((1)/(epsilon2) log(2) epsilonN) space where N is the number of data items in the window. We extend this framework to further process generalized constrained sliding window queries and proved that our technique is applicable for flexible window settings. Our performance study indicates that the space required in practice is much less than the given theoretical bound and the algorithm supports high speed data streams.

Discovering user access pattern based on probabilistic latent factor model

There has been an increased demand for characterizing user access patterns using web mining techniques since the informative knowledge extracted from web server log files can not only offer benefits for web site structure improvement but also for better understanding of user navigational behavior. In this paper, we present a web usage mining method, which utilize web user usage and page linkage information to capture user access pattern based on Probabilistic Latent Semantic Analysis (PLSA) model. A specific probabilistic model analysis algorithm, EM algorithm, is applied to the integrated usage data to infer the latent semantic factors as well as generate user session clusters for revealing user access patterns. Experiments have been conducted on real world data set to validate the effectiveness of the proposed approach. The results have shown that the presented method is capable of characterizing the latent semantic factors and generating user profile in terms of weighted page vectors, which may reflect the common access interest exhibited by users among same session cluster.