RESOURCE ALLOCATION IN JOINT PUBLIC-PRIVATE AGRICULTURAL RESEARCH

Federal technology transfer legislation has encouraged increased collaboration between the public and private sectors, including joint research ventures known as Cooperative Research and Development Agreements (CRADAs). While several economically important technologies have been developed through CRADAs, there is concern that CRADA may divert public research from its central research missions. This study compares the pattern of research resource allocation for CRADA projects at the U.S. Department of Agriculture with research priorities of public and private intramural agricultural research. The findings suggest that CRADAs have attracted considerable private co-financing of joint research projects, and may have enabled public research to concentrate more resources on research areas where private incentives are relatively weak.agricultural research, CRADA projects, research priorities, technology transfer, Research and Development/Tech Change/Emerging Technologies,

Flucytosine and cryptococcosis: time to urgently address the worldwide accessibility of a 50-year-old antifungal.

Current, widely accepted guidelines for the management of HIV-associated cryptococcal meningoencephalitis (CM) recommend amphotericin B combined with flucytosine (5-FC) for ≥2 weeks as the initial induction treatment of choice. However, access to flucytosine in Africa and Asia, where disease burden is greatest, is inadequate at present. While research into identifying effective and well-tolerated antifungal combinations that do not contain flucytosine continues, an ever-increasing body of evidence from in vitro, in vivo and clinical studies points to the benefits of flucytosine in the treatment of CM in both intravenous combinations with amphotericin B and oral combinations with high-dose fluconazole. This article provides an up-to-date review of this evidence, and the current issues and challenges regarding increasing access to this key component of combination antifungal therapy for cryptococcosis

Explicit Building Block Multiobjective Evolutionary Computation: Methods and Applications

This dissertation presents principles, techniques, and performance of evolutionary computation optimization methods. Concentration is on concepts, design formulation, and prescription for multiobjective problem solving and explicit building block (BB) multiobjective evolutionary algorithms (MOEAs). Current state-of-the-art explicit BB MOEAs are addressed in the innovative design, execution, and testing of a new multiobjective explicit BB MOEA. Evolutionary computation concepts examined are algorithm convergence, population diversity and sizing, genotype and phenotype partitioning, archiving, BB concepts, parallel evolutionary algorithm (EA) models, robustness, visualization of evolutionary process, and performance in terms of effectiveness and efficiency. The main result of this research is the development of a more robust algorithm where MOEA concepts are implicitly employed. Testing shows that the new MOEA can be more effective and efficient than previous state-of-the-art explicit BB MOEAs for selected test suite multiobjective optimization problems (MOPs) and U.S. Air Force applications. Other contributions include the extension of explicit BB definitions to clarify the meanings for good single and multiobjective BBs. A new visualization technique is developed for viewing genotype, phenotype, and the evolutionary process in finding Pareto front vectors while tracking the size of the BBs. The visualization technique is the result of a BB tracing mechanism integrated into the new MOEA that enables one to determine the required BB sizes and assign an approximation epistasis level for solving a particular problem. The culmination of this research is explicit BB state-of-the-art MOEA technology based on the MOEA design, BB classifier type assessment, solution evolution visualization, and insight into MOEA test metric validation and usage as applied to test suite, deception, bioinformatics, unmanned vehicle flight pattern, and digital symbol set design MOPs

A Multiobjective Approach Applied to the Protein Structure Prediction Problem

Interest in discovering a methodology for solving the Protein Structure Prediction problem extends into many fields of study including biochemistry, medicine, biology, and numerous engineering and science disciplines. Experimental approaches, such as, x-ray crystallographic studies or solution Nuclear Magnetic Resonance Spectroscopy, to mathematical modeling, such as minimum energy models are used to solve this problem. Recently, Evolutionary Algorithm studies at the Air Force Institute of Technology include the following: Simple Genetic Algorithm (GA), messy GA, fast messy GA, and Linkage Learning GA, as approaches for potential protein energy minimization. Prepackaged software like GENOCOP, GENESIS, and mGA are in use to facilitate experimentation of these techniques. In addition to this software, a parallelized version of the fmGA, the so-called parallel fast messy GA, is found to be good at finding semi-optimal answers in reasonable wall clock time. The aim of this work is to apply a Multiobjective approach to solving this problem using a modified fast messy GA. By dividing the CHARMm energy model into separate objectives, it should be possible to find structural configurations of a protein that yield lower energy values and ultimately more correct conformations

07-01-1983 Memorandum to the Conference

With reference to the effective date of the judgment, Frank Lorson suggested the following language could be added as the last sentence of the per curiam if the Conference so desires

Many-body theory interpretation of deep inelastic scattering

We analyze data on deep inelastic scattering of electrons from the proton using ideas from standard many-body theory involving {\em bound} constituents subject to {\em interactions}. This leads us to expect, at large three-momentum transfer ${\bf{q}}$, scaling in terms of the variable $\tilde{y}=\nu-{\bf |q|}$. The response at constant ${\bf |q|}$ scales well in this variable. Interaction effects are manifestly displayed in this approach. They are illustrated in two examples.Comment: 10 pages, 4 figure