Adenosine to inosine editing by ADAR2 requires formation of a ternary complex on the GluR-B R/G site

RNA editing by members of the ADAR (adenosine deaminase that acts on RNA) enzyme family involves hydrolytic deamination of adenosine to inosine within the context of a double-stranded pre-mRNA substrate. Editing of the human GluR-B transcript is catalyzed by, the enzyme ADAR2 at the Q/R and R/G sites. We have established a minimal RNA substrate for editing based on the RIG site and have characterized the interaction of ADAR2 with this RNA by gel shift, kinetic, and cross-linking analyses. Gel shift analysis revealed that two complexes are formed on the RNA as protein concentration is increased; the ADAR monomers can be crosslinked to one another in an RNA-dependent fashion. We performed a detailed kinetic study of the editing reaction; the data from this study are consistent with a reaction scheme in which formation of an ADAR2.RNA ternary complex is required for efficient RNA editing and in which formation of this complex is rate determining. These observations suggest that RNA adenosine deaminases function as homodimers on their RNA substrates and may partially explain regulation of RNA editing in these systems

Using Agent-Based Modeling to Simulate the Foreclosure Contagion Effect

A foreclosed property can have a negative impact on the prices of other properties within its neighborhood and these reduced property prices can lead to further foreclosures within the neighborhood; this is known as the foreclosure contagion effect. This effect has been demonstrated, within the real estate literature, to occur. Traditionally, real estate research have used statistical regression to analysis this issues. The application of Agent-based Modeling and Simulation (ABMS) has risen in the last 15 years and has successfully been used to model complexity situations, e.g., the real estate market. ABMS offers a way to explore the impact of different factors on the real estate market without having to experiment on real-world systems. This paper looks at application of ABMS to investigate the foreclosure contagion effect

Hedonic Games and Monte Carlo Simulation

Hedonic games have applications in economics and multi-agent systems where the grouping preferences of an individual is important. Hedonic games look at coalition formation, amongst the players, where players have a preference relation over all the coalition. Hedonic games are also known as coalition formation games, and they are a form of a cooperative game with a non-transferrable utility game. Some examples of hedonic games are stable marriage, stable roommate, and hospital/residence problem. The study of hedonic games is driven by understanding what coalition structures will be stable, i.e., given a coalition structure, no players have an incentive to deviate to or form another coalition. Different solution concepts exist for solving hedonic games; the one that we use in our study is core stability. From the computational perspective, finding any stable coalition structure of a hedonic game is challenging. In this research, we use Monte Carlo methods to find the solution of millions of hedonic with the hope of finding some empirical points of interest. We aim to explore the distribution of the number of stable coalition structures for a given randomly generated hedonic game and to analyze that distribution using Cullen and Frey graph approach

Is Explainability Always Necessary? Discussion on Explainable AI

The explainability of a model has been a topic of debate. Some research states explainability is unnecessary, and some ”white-box” models, such as regression models or decision trees, are inherently explainable. This paper conducts a multiple regression model analysis with highly correlated features to illustrate how the model’s explainability fails when dealing with complex data. In this case, trusting the model explanations can be problematic. The Shapley net effect technique, which helps determine the marginal contribution of the features, is employed to improve the model explainability and reveal more information about the prediction. The work concludes that explainability is necessary to avoid biased and erroneous conclusions in all circumstances, including simple models or even more apparent cases

A Discussion on Supplier Selection Modeling Approaches

Supplier selection is a subfield of supply chain management that involves multiple steps in order for decision-makers to find suitable suppliers. Supplier selection is important as it could influence the whole company positively or negatively. It has, recently, become a topic of interest because of the recent pandemic and its effect on the global supply chain, which causes supply shortages. As such, the focus of this paper is on characteristics of decision-making modeling approaches, specifically agent-based modeling and multi-agent systems, in supplier selection, as its modeling has always been a challenge for companies due to its complex nature