Immunogens and Antigen Processing: Report from a Global HIV Vaccine Enterprise Working Group

The Global HIV Vaccine Enterprise convened a meeting of a Working Group in July 2009 to discuss recent progress in rational design of the components of an HIV vaccine, such as inserts, vectors and adjuvants,and in understanding antigen processing and presentation to T and B cells. This Report summarizes the key points of that discussion, and subsequent discussions with the Chairs of the other Enterprise Working Groups, the Enterprise Science Committee, the Enterprise Council and the broader scientific community during open sessions at scientific conferences

The Application of Integrated Knowledge-based Systems for the Biomedical Risk Assessment Intelligent Network (BRAIN)

One of NASA's goals for long duration space flight is to maintain acceptable levels of crew health, safety, and performance. One way of meeting this goal is through the Biomedical Risk Assessment Intelligent Network (BRAIN), an integrated network of both human and computer elements. The BRAIN will function as an advisor to flight surgeons by assessing the risk of in-flight biomedical problems and recommending appropriate countermeasures. This paper describes the joint effort among various NASA elements to develop BRAIN and an Infectious Disease Risk Assessment (IDRA) prototype. The implementation of this effort addresses the technological aspects of the following: (1) knowledge acquisition; (2) integration of IDRA components; (3) use of expert systems to automate the biomedical prediction process; (4) development of a user-friendly interface; and (5) integration of the IDRA prototype and Exercise Countermeasures Intelligent System (ExerCISys). Because the C Language, CLIPS (the C Language Integrated Production System), and the X-Window System were portable and easily integrated, they were chosen as the tools for the initial IDRA prototype. The feasibility was tested by developing an IDRA prototype that predicts the individual risk of influenza. The application of knowledge-based systems to risk assessment is of great market value to the medical technology industry

Adapting Artificial Immune Algorithms For University Timetabling

Penjadualan kelas dan peperiksaan di universiti adalah masalah pengoptimuman berkekangan tinggi. University class and examination timetabling are highly constrained optimization problems

Population-Based Optimization Algorithms for Solving the Travelling Salesman Problem

[Extract] Population based optimization algorithms are the techniques which are in the set of the nature based optimization algorithms. The creatures and natural systems which are working and developing in nature are one of the interesting and valuable sources of inspiration for designing and inventing new systems and algorithms in different fields of science and technology. Evolutionary Computation (Eiben& Smith, 2003), Neural Networks (Haykin, 99), Time Adaptive Self-Organizing Maps (Shah-Hosseini, 2006), Ant Systems (Dorigo & Stutzle, 2004), Particle Swarm Optimization (Eberhart & Kennedy, 1995), Simulated Annealing (Kirkpatrik, 1984), Bee Colony Optimization (Teodorovic et al., 2006) and DNA Computing (Adleman, 1994) are among the problem solving techniques inspired from observing nature. In this chapter population based optimization algorithms have been introduced. Some of these algorithms were mentioned above. Other algorithms are Intelligent Water Drops (IWD) algorithm (Shah-Hosseini, 2007), Artificial Immune Systems (AIS) (Dasgupta, 1999) and Electromagnetism-like Mechanisms (EM) (Birbil & Fang, 2003). In this chapter, every section briefly introduces one of these population based optimization algorithms and applies them for solving the TSP. Also, we try to note the important points of each algorithm and every point we contribute to these algorithms has been stated. Section nine shows experimental results based on the algorithms introduced in previous sections which are implemented to solve different problems of the TSP using well-known datasets

Leo: Lagrange Elementary Optimization

Global optimization problems are frequently solved using the practical and efficient method of evolutionary sophistication. But as the original problem becomes more complex, so does its efficacy and expandability. Thus, the purpose of this research is to introduce the Lagrange Elementary Optimization (Leo) as an evolutionary method, which is self-adaptive inspired by the remarkable accuracy of vaccinations using the albumin quotient of human blood. They develop intelligent agents using their fitness function value after gene crossing. These genes direct the search agents during both exploration and exploitation. The main objective of the Leo algorithm is presented in this paper along with the inspiration and motivation for the concept. To demonstrate its precision, the proposed algorithm is validated against a variety of test functions, including 19 traditional benchmark functions and the CECC06 2019 test functions. The results of Leo for 19 classic benchmark test functions are evaluated against DA, PSO, and GA separately, and then two other recent algorithms such as FDO and LPB are also included in the evaluation. In addition, the Leo is tested by ten functions on CECC06 2019 with DA, WOA, SSA, FDO, LPB, and FOX algorithms distinctly. The cumulative outcomes demonstrate Leo's capacity to increase the starting population and move toward the global optimum. Different standard measurements are used to verify and prove the stability of Leo in both the exploration and exploitation phases. Moreover, Statistical analysis supports the findings results of the proposed research. Finally, novel applications in the real world are introduced to demonstrate the practicality of Leo.Comment: 28 page

Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex