System modeling is a widely used technique to model state-based systems. Several state-based languages are used to model such systems, e.g., EFSM, SDL and State Charts. Although state-based modeling is very useful, system models are frequently large and complex and are hard to understand and modify. Slicing is a well-known reduction technique. Most of the research on slicing is code-based.There has been limited research on specification-based slicing and model-based slicing. In this paper, we present an approach to slicing state-based models, in particular EFSM models. Our approach automatically identifies the parts of the model that affect an element of interest using EFSM dependence analysis. Slice reduction techniques are then used to reduce the size of the EFSM slice. Our experience with the presented slicing approach showed that significant reduction of state-based models could be achieved.

Transdermal drug delivery (TDD) is a technique that is used to deliver a drug into the systemic circulation across the skin. This mechanism of drug delivery route has many advantages, including steady drug plasma concentrations, improved patient compliance, elimination of hepatic first pass, and degradation in the gastrointestinal tract. Over the last 30 years, many transdermal products have been launched in the market. Despite the inherent advantages of TDD and the growing list of transdermal products, one of the major drawbacks to TDD is the occurrence of inter- and intraindividual variation in the absorption of the drug across the skin. A majority of these variations are caused by biological factors, such as gender, age, ethnicity, and skin hydration and metabolism. These factors affect the integrity and the barrier qualities of the skin, which subsequently result in the variation in the amount of drug absorbed. The main objective of this review article is to provide a concise commentary on the biological factors that contribute to the variation in transdermal permeation of drugs across human skin and the available transdermal therapeutic systems that may reduce the variations caused by biological factors.

An early and accurate diagnosis of reproductive dysfunctions or aberrations is crucial to better reproductive management in livestock. High reproductive efficiency is a prerequisite for high life-time production in dairy animals. Early pregnancy diagnosis is key to shorten the calving interval through early identification of open animals and their timely treatment and rebreeding so as to maintain a postpartum barren interval close to 60 days. A buffalo, the most important dairy animal in the Indian subcontinent, is known for problems related to high calving interval, late puberty, and high incidence of anestrus. Lack of reliable cow-side early pregnancy diagnosis methods further aggravates the situation. Several methods of pregnancy diagnosis are being practiced in bovine species, yet none qualifies as the ideal pregnancy diagnosis method due to the inherent limitations of sensitivity, accuracy, specificity, speed, and ease of performing the test. The advancement of molecular techniques like proteomics and their applications in animal research has given a new hope to look for pregnancy biomarker molecules in these animals. This review attempts to examine common pregnancy diagnosis methods available for dairy animals, while assessing the usefulness of the modern technologies in detecting novel pregnancy markers and designing future strategies for research in this area.

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