SLIDES: NEPA and Public Participation in Grazing Management on Federal Public Lands: The 40-Year Struggle

Presenter: Joe Feller, College of Law, Arizona State University 22 slide

SLIDES: Livestock Grazing on the Public Lands

Presenter: Joe Feller, Professor of Law, Arizona State University Law School; Visiting Professor, University of Colorado Law School 33 slide

SLIDES: Livestock Grazing on the Public Lands

Presenter: Joe Feller, Professor of Law, Arizona State University Law School; Visiting Professor, University of Colorado Law School 33 slide

LARGE CROWDS OR LARGE INVESTMENTS? HOW SOCIAL IDENTITY INFLUENCES THE COMMITMENT OF THE CROWD

Equity crowdfunding is increasing in popularity as an alternative to traditional financing for start-ups and growth companies to raise money for their business. This study discusses how equity crowdfunding is different from traditional financing, such as angel investors and venture capitalists. We argue this difference is brought further into focus when large numbers of crowd members invest small amounts, as opposed to fewer individuals making large investments. Building on existing research on Social Identity Theory, we look at why some crowdfunding campaigns are more likely to attract these contrasting types of investment (numerous small investments or fewer large investments). A model is presenting linking different characteristics of campaigns to total investment and average investment. This proposed model will be tested using public data gathered from Crowdcube, a leading UK-based equity crowdfunding platform. This study has significant implications for fundraisers who may wish to target different types of crowds according to the nature of their business, i.e. smaller numbers of passionate investors to provide informed input or larger numbers of casual investors to help create awareness and spread positive word of mouth

Genome Wide DNA Copy Number Analysis of Serous Type Ovarian Carcinomas Identifies Genetic Markers Predictive of Clinical Outcome

Ovarian cancer is the fifth leading cause of cancer death in women. Ovarian cancers display a high degree of complex genetic alterations involving many oncogenes and tumor suppressor genes. Analysis of the association between genetic alterations and clinical endpoints such as survival will lead to improved patient management via genetic stratification of patients into clinically relevant subgroups. In this study, we aim to define subgroups of high-grade serous ovarian carcinomas that differ with respect to prognosis and overall survival. Genome-wide DNA copy number alterations (CNAs) were measured in 72 clinically annotated, high-grade serous tumors using high-resolution oligonucleotide arrays. Two clinically annotated, independent cohorts were used for validation. Unsupervised hierarchical clustering of copy number data derived from the 72 patient cohort resulted in two clusters with significant difference in progression free survival (PFS) and a marginal difference in overall survival (OS). GISTIC analysis of the two clusters identified altered regions unique to each cluster. Supervised clustering of two independent large cohorts of high-grade serous tumors using the classification scheme derived from the two initial clusters validated our results and identified 8 genomic regions that are distinctly different among the subgroups. These 8 regions map to 8p21.3, 8p23.2, 12p12.1, 17p11.2, 17p12, 19q12, 20q11.21 and 20q13.12; and harbor potential oncogenes and tumor suppressor genes that are likely to be involved in the pathogenesis of ovarian carcinoma. We have identified a set of genetic alterations that could be used for stratification of high-grade serous tumors into clinically relevant treatment subgroups

Bivariate mixed distribution with a heavy-tailed component and its application to single-site daily rainfall simulation

This paper presents an improved bivariate mixed distribution, which is capable of modeling the dependence of daily rainfall from two distinct sources (e.g., rainfall from two stations, two consecutive days, or two instruments such as satellite and rain gauge). The distribution couples an existing framework for building a bivariate mixed distribution, the theory of copulae and a hybrid marginal distribution. Contributions of the improved distribution are twofold. One is the appropriate selection of the bivariate dependence structure from a wider admissible choice (10 candidate copula families). The other is the introduction of a marginal distribution capable of better representing low to moderate values as well as extremes of daily rainfall. Among several applications of the improved distribution, particularly presented here is its utility for single-site daily rainfall simulation. Rather than simulating rainfall occurrences and amounts separately, the developed generator unifies the two processes by generalizing daily rainfall as a Markov process with autocorrelation described by the improved bivariate mixed distribution. The generator is first tested on a sample station in Texas. Results reveal that the simulated and observed sequences are in good agreement with respect to essential characteristics. Then, extensive simulation experiments are carried out to compare the developed generator with three other alternative models: the conventional two-state Markov chain generator, the transition probability matrix model, and the semiparametric Markov chain model with kernel density estimation for rainfall amounts. Analyses establish that overall the developed generator is capable of reproducing characteristics of historical extreme rainfall events and is apt at extrapolating rare values beyond the upper range of available observed data. Moreover, it automatically captures the persistence of rainfall amounts on consecutive wet days in a relatively natural and easy way. Another interesting observation is that the recognized “overdispersion” problem in daily rainfall simulation ascribes more to the loss of rainfall extremes than the under-representation of first-order persistence. The developed generator appears to be a sound option for daily rainfall simulation, especially in particular hydrologic planning situations when rare rainfall events are of great importance