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Arctic peoples today find themselves on the front line of rapid environmental change brought about by globalizing forces, shifting climates, and destabilizing physical condi- tions. The weather is not the only thing undergoing rapid change here. Social climates are intrinsically connected to physical climates, and changes within each have profound effects on the daily life, health, and well-being of circumpolar indigenous peoples. This paper describes a collaborative effort between university researchers and community members from five indigenous communities in the circumpolar north aimed at compar- ing the experiences of indigenous Arctic youth in order to come up with a shared model of indigenous youth resilience. The discussion introduces a sliding scale model that emerged from the comparative data analysis. It illustrates how a “sliding scale” of resilience captures the inherent dynamism of youth strategies for “doing well” and what forces represent positive and negative influences that slide towards either personal and communal resilience or vulnerability. The model of the sliding scale is designed to reflect the contingency and interdependence of resilience and vulnerability and their fluctuations between lowest and highest points based on timing, local situ- ation, larger context, and meaning.

Childhood osteogenic sarcoma (OS) is a rare bone cancer occurring primarily in adolescents. The North American pediatric cooperative groups have performed a series of clinical treatment trials in this disease over the past several decades, and biology studies of tumor tissue have been an important study component. A meeting was held in Bethesda, Maryland on November 29-30, 2001, sponsored by the NIH Office of Rare Diseases, the Children's Oncology Group, and the National Cancer Institute-Cancer Therapy Evaluation Program with the general objectives: (a) to review the current state of knowledge regarding OS biology; (b) to identify, prioritize, and support the development of biology studies of potential clinical relevance in OS; and (c) to discuss the available tissue resources and the appropriate methods for analysis of OS samples for the conduct of biology studies. This report summarizes the information presented and discussed by the meeting participants.

It is no surprise that population plays an important role in long-term models of greenhouse gas emissions and global climate change. Population represents both a primary scale factor for the size of the human economy and the fundamental unit at which societal welfare is measured. This paper surveys the population assumptions in several important models of global warming in the 1990s, including energy models, integrated emissions models, and economic policy models. Choice of population inputs, the role of population variables in model equations, sensitivity analyses, and consideration of population policy are all described. The paper finds room for improvement in the following areas: choice and provision of population projections, consistent treatment of age structure and urbanization, specification of relationship between per capita income and population, and the consideration of population policy in economic policy models. It is suggested that improvement in these areas would be greatly aided by interdisciplinary cooperation, especially between demographers, economists, and energy modelers.

Demographic Impact (DI) models are multiplicative identities used to decompose environmental impacts into components due to population, economic and technological change. While such decompositions have played an important role in the population-environment literature, inconsistent and ambiguous methods have made their results difficult to interpret and nearly impossible to compare. This paper demonstrates and clarifies the differences between these methods using the example of anthropogenic greenhouse gas (GHG) emissions. This paper locates two distinct approaches to DI model decomposition: The annual growth rate decomposition and the multiplicative decomposition. Stable indices are provided for each approach. Finally, the Divisia Index -- a common price index used by economists -- is suggested as an appropriate method to aggregate DI model results.

It is now almost two decades since the first human cardiac transplantation was performed. Recipients will require close follow-up by their referring physicians outside of the main referral centers. This article is intended to assist the referring physician in choosing the most appropriate diagnostic studies throughout the posttransplant period.