The revival of East European nationalisms

"July 10, 1965.""Prepared for the Fifth International Conference on World Politics, Noordwijk, Netherlands, September 13-18, 1965.""1605"--handwritten on coverIncludes bibliographical reference

The left in France, Italy, and Spain

"April 1979.""2237"--handwritten on cover"This will be the opening chapter of William E. Griffith, ed., The Left in France, Italy, and Spain (Lexington, Mass.: Lexington Books, to be published this year.)"Includes bibliographical reference

Middle East and the great powers

"November 8, 1976.""#2127"--handwritten on coverIncludes bibliographical reference

African crises and American policy

"1565"--handwritten on coverIncludes bibliographical rererence

Probability distributions for economic surplus changes: the case of technical change in the Australian wool industry

Mullen, Alston and Wohlgenant (1989) (MAW) examined the distribution of the benefits of technical change in the Australian wool industry. Their conclusions are revisited by examining the probability distributions of changes in the welfare measures, given uncertainty about their model parameters. Subjective probability distributions are specified for the parameters and correlations among some of the parameters are imposed. Hierarchical distributions are also used to model diverse views about the specification of the subjective distributions. A sensitivity elasticity is defined through the estimation of a response surface to measure the sensitivity of the estimated research benefits to individual parameters. MAW’s conclusions are found to be robust under the stochastic approach to sensitivity analysis demonstrated in this article.Livestock Production/Industries,

The Middle East, 1982 : politics, revolutionary Islam, and American policy

"January 20, 1982.""#2365"--handwritten on coverIncludes bibliographical reference

Soviet-U.S. rivalry in Southern Europe

"November 8, 1976."#2126"--handwritten on coverIncludes bibliographical reference

Best practices for bioinformatic characterization of neoantigens for clinical utility

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types

Experimental Evidence for Different Strain Regimes of Crack Populations in a Clay Model

We report results from clay extension experiments used as a model for the evolution of fault populations due to stress interactions. At yielding cracks begin to appear and the brittle strain due to them quickly reaches a rate matching the applied stretching rate. The crack density (number of cracks per unit area) initially increases apace, then reaches a maximum at a critical strain, decreasing thereafter. At low strains, where the crack population is dilute, a power law length distribution is observed, which at high strain, gradually transitions to an exponential. This agrees with fault populations data observed in low and high strain settings. These results indicate that fault populations ranging from power law to exponential size-frequency distributions reflect the population evolution with increased strain