Interstellar extinction at 10-20 microns

The IRAS low-resolution spectra (LRS) spectra of 117 stars of excellent signal/noise with optically thin silicate dust shells were analyzed. The stellar continua (assumed to be a cool black body) were subtracted, and the resulting dust shell spectra were fit with simple models F(sub lambda) assuming uniform mass loss and dust temperature as a function of distance from the star, calculated using the optical constants for silcates of Draine (1985). From the comparison of the spectra and the models, functions for the emissivity, kappa(sub lambda), were derived

Measuring Operations: An Analysis of the Financial Statements of U.S. Private Colleges and Universities

As events in the business sector have highlighted, companies can play by the rules and yet produce misleading financial statements. This study examines the nongovernmental organizations that provide a substantial portion of higher education in the United States. We seek to determine whether private colleges and universities take advantage of the discretion available to them under accounting and auditing standards by presenting an operating measure in their statement of activities. We find that nearly 60 percent of schools report an operating measure but the items included or excluded from operations vary widely.This publication is Hauser Center Working Paper No. 17. The Hauser Center Working Paper Series was launched during the summer of 2000. The Series enables the Hauser Center to share with a broad audience important works-in-progress written by Hauser Center scholars and researchers

Assessing Financial Vulnerability in the Nonprofit Sector

Effective nonprofit governance relies upon understanding an organization's financial condition and vulnerabilities. However, financial vulnerability of nonprofit organizations is a relatively new area of study. In this paper, we compare two models used to forecast bankruptcy in the corporate sector (Altman 1968 and Ohlson 1980) with the model used by nonprofit researchers (Tuckman and Chang 1991). We find that the Ohlson model has higher explanatory power than either Tuckman and Chang's or Altman's in predicting four different measures of financial vulnerability. However, we show that none of the models, individually or combined, are effective in predicting financial distress. We then propose a more comprehensive model of financial vulnerability by adding two new variables to represent reliance on commercial-type activities to generate revenues and endowment sufficiency. We find that this model outperforms Ohlson's model and performs substantially better in explaining and predicting financial vulnerability. Hence, the expanded model can be used as a guide for understanding the drivers of financial vulnerability and for identifying more effective proxies for nonprofit sector financial distress for use in future research. This publication is Hauser Center Working Paper No. 27. The Hauser Center Working Paper Series was launched during the summer of 2000. The Series enables the Hauser Center to share with a broad audience important works-in-progress written by Hauser Center scholars and researchers

Using electrostatic potentials to predict DNA-binding sites on DNA-binding proteins

A method to detect DNA-binding sites on the surface of a protein structure is important for functional annotation. This work describes the analysis of residue patches on the surface of DNA-binding proteins and the development of a method of predicting DNA-binding sites using a single feature of these surface patches. Surface patches and the DNA-binding sites were initially analysed for accessibility, electrostatic potential, residue propensity, hydrophobicity and residue conservation. From this, it was observed that the DNA-binding sites were, in general, amongst the top 10% of patches with the largest positive electrostatic scores. This knowledge led to the development of a prediction method in which patches of surface residues were selected such that they excluded residues with negative electrostatic scores. This method was used to make predictions for a data set of 56 non-homologous DNA-binding proteins. Correct predictions made for 68% of the data set