Comparison of porcine urine and bile as matrices to screen for the residues of two sulfonamides using a semi-automated enzyme immunoassay.

Porcine urine enzyme immunoassays for sulfamethazine and sulfadiazine have previously been employed as screening tests to predict the concentrations of the drugs in the corresponding tissues (kidneys), If a urine was found positive (> 800 ng ml(-1)) the corresponding kidney was then analysed by an enzyme immunoassay and, if found positive, a confirmatory analysis by HPLC was performed. Urine was chosen as the screening matrix since sulfonamides are mainly eliminated through this body fluid, However, after obtaining a number of false positive predictions, an investigation was carried out to assess the possibility of using an alternative body fluid which would act as a superior indicator of the presence of sulfonamides in porcine kidney, An initial study indicated that serum, plasma and bile could all be used as screening matrices. From these, bile was chosen as the preferred sample matrix and an extensive study followed to compare the efficiencies of sulfonamide positive bile and urine at predicting sulphonamide positive kidneys, Bile was found to be 17 times more efficient than urine at predicting a sulfamethazine positive kidney and 11 times more efficient at predicting a sulfadiazine positive kidney, With this enhanced performance of the initial screening test, the need for the costly and time consuming kidney enzyme immunoassay, prior to HPLC analysis, was eliminated

Pharmacokinetics of UC781-loaded intravaginal ring segments in rabbits: a comparison of polymer matrices

UC781 is a potent and poorly water-soluble nonnucleoside reverse transcriptase inhibitor being investi- gated as a potential microbicide for preventing sexual transmission of HIV-1. This study was designed to evaluate the in vivo release and pharmacokinetics of UC781 delivered from matrix-type intravaginal ring segments in rabbits. Three polymer matrices (polyurethane, ethylene vinyl acetate copolymer, and silicone elastomer) and two drug loadings (5 and 15 mg/segment) were evaluated in at least one of two independent studies for up to 28 days in vivo. Inter-study comparison of in vivo release, vaginal tissue, and plasma concentrations for similar formulations demonstrated good reproducibility of the animal model. Mean estimates for a 28-day in vivo release ranged from 0.35 to 3.17 mg UC781 per segment. Mean proximal vaginal tissue levels (adjacent to the IVR segment) were 8– 410 ng/g and did not change significantly with time for most formulations. Distal vaginal tissue levels of UC781 were 6- to 49-fold lower than proximal tissue levels. Mean UC781 plasma levels were low for all formulations, at 0.09–0.58 ng/mL. All formulations resulted in similar UC781 concentrations in vaginal tissue and plasma, except the low loading polyurethane group which provided significantly lower levels. Loading dependent release and pharmacokinetics were only clearly observed for the polyurethane matrix. Based on these results, intravaginal ring segments loaded with UC781 led to vaginal tissue concen- trations ranging from below to approximately two orders of magnitude higher than UC781’s EC50 under in vitro conditions (2.8 ng/mL), with little influence by polymer matrix or UC781 loading. Moreover, these findings support the use of rabbit vaginal pharmacokinetic studies in preclinical testing of microbicide intravaginal rings.

Distribution of the Demmel Condition Number of Wishart Matrices

This paper studies the Demmel condition number of Wishart matrices, a quantity which has numerous applications to wireless communications, such as adaptive switching between beamforming and diversity coding, link adaptation, and spectrum sensing. For complex Wishart matrices, we give an exact analytical expression for the probability density function (p.d.f.) of the Demmel condition number, and also derive simplified expressions for the high tail regime. These results indicate that the condition of complex Wishart matrices is dominantly decided by the difference between the matrix dimension and degree of freedom (DoF), i.e., the probability of drawing a highly ill conditioned matrix decreases considerably when the difference between the matrix dimension and DoF increases. We further investigate real Wishart matrices, and derive new expressions for the p.d.f. of the smallest eigenvalue, when the difference between the matrix dimension and DoF is odd. Based on these results, we succeed to obtain an exact p.d.f. expression for the Demmel condition number, and simplified expressions for the high tail regime.

Determination of thyreostat residues from bovine matrices using high-performance liquid chromatography

High-performance liquid chromatography (HPLC) methodologies were evaluated for the detection and quantification of thyreostatic drug residues in cattle serum and thyroid tissue. The paper details a protocol, using a simple ethyl acetate extraction for the determination of thiouracil, tapazole, methyl thiouracil, propyl thiouracil and phenyl thiouracil in thyroid tissue. Using two sequential HPLC injections, and quantitative analysis, in two steps, all five thyreostats were detectable at concentrations greater than 2.45-4.52 ng/g. Modifications to a published method for detection of thyreostatic residues in serum involving the addition of mercaptoethanol and a freezing step are described. The modifications improved sensitivity and allowed detection of the five thyreostats at levels greater than 16.98-35.25 ng/ml. Young bulls were treated with thyreostats to demonstrate the validity of the methodologies described. Administered thyreostats were not absorbed equally by the test animals and the compounds were not all detected in the serum samples removed at 7 days following drug withdrawal. These experiments indicate the necessity to be able to detect thyreostat residues in a variety of matrices. (C) 1998 Elsevier Science B.V. All rights reserved.

Robust visual tracking based on online learning sparse representation

Handling appearance variations is a very challenging problem for visual tracking. Existing methods usually solve this problem by relying on an effective appearance model with two features: (1) being capable of discriminating the tracked target from its background, (2) being robust to the target's appearance variations during tracking. Instead of integrating the two requirements into the appearance model, in this paper, we propose a tracking method that deals with these problems separately based on sparse representation in a particle filter framework. Each target candidate defined by a particle is linearly represented by the target and background templates with an additive representation error. Discriminating the target from its background is achieved by activating the target templates or the background templates in the linear system in a competitive manner. The target's appearance variations are directly modeled as the representation error. An online algorithm is used to learn the basis functions that sparsely span the representation error. The linear system is solved via ℓ1 minimization. The candidate with the smallest reconstruction error using the target templates is selected as the tracking result. We test the proposed approach using four sequences with heavy occlusions, large pose variations, drastic illumination changes and low foreground-background contrast. The proposed approach shows excellent performance in comparison with two latest state-of-the-art trackers.

A sequential algorithm for sparse support vector classifiers

Support vector machines (SVMs), though accurate, are not preferred in applications requiring high classification speed or when deployed in systems of limited computational resources, due to the large number of support vectors involved in the model. To overcome this problem we have devised a primal SVM method with the following properties: (1) it solves for the SVM representation without the need to invoke the representer theorem, (2) forward and backward selections are combined to approach the final globally optimal solution, and (3) a criterion is introduced for identification of support vectors leading to a much reduced support vector set. In addition to introducing this method the paper analyzes the complexity of the algorithm and presents test results on three public benchmark problems and a human activity recognition application. These applications demonstrate the effectiveness and efficiency of the proposed algorithm.


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On the Condition Number Distribution of Complex Wishart Matrices

This paper investigates the distribution of the condition number of complex Wishart matrices. Two closely related measures are considered: the standard condition number (SCN) and the Demmel condition number (DCN), both of which have important applications in the context of multiple-input multipleoutput (MIMO) communication systems, as well as in various branches of mathematics. We first present a novel generic framework for the SCN distribution which accounts for both central and non-central Wishart matrices of arbitrary dimension. This result is a simple unified expression which involves only a single scalar integral, and therefore allows for fast and efficient computation. For the case of dual Wishart matrices, we derive new exact polynomial expressions for both the SCN and DCN distributions. We also formulate a new closed-form expression for the tail SCN distribution which applies for correlated central Wishart matrices of arbitrary dimension and demonstrates an interesting connection to the maximum eigenvalue moments of Wishart matrices of smaller dimension. Based on our analytical results, we gain valuable insights into the statistical behavior of the channel conditioning for various MIMO fading scenarios, such as uncorrelated/semi-correlated Rayleigh fading and Ricean fading. © 2010 IEEE.

Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding

In this paper, we introduce an application of matrix factorization to produce corpus-derived, distributional
models of semantics that demonstrate cognitive plausibility. We find that word representations
learned by Non-Negative Sparse Embedding (NNSE), a variant of matrix factorization, are sparse,
effective, and highly interpretable. To the best of our knowledge, this is the first approach which
yields semantic representation of words satisfying these three desirable properties. Though extensive
experimental evaluations on multiple real-world tasks and datasets, we demonstrate the superiority
of semantic models learned by NNSE over other state-of-the-art baselines.