Famine-related mortality in early life and accelerated life histories in nineteenth-century Belgium

Density-dependent and extrinsic mortality are predicted to accelerate reproductive maturation. The first 5 years of life is a proposed sensitive period for life-history regulation. This study examines the ways in which local mortality during this sensitive period was related to subsequent marriage timing in nineteenth-century Belgium (n women = 11 892; n men = 14 140). Local mortality during the sensitive period was inversely associated with age at first marriage for men and women controlling for literacy, occupational status, population growth and migration. Cox regression indicated decreased time to marriage for women (HR = 1.661, 95% CI: 1.542–1.789) and men (HR = 1.327, 95% CI: 1.238–1.422) from high mortality municipalities. Rising population growth rates were associated with earlier marriage for men and women. Migration in general was associated with later marriage for men and women. Consistent with life-history predictions, harsh ecological conditions during early life such as famine coincided with earlier marriage.     

Legacies in life histories

Complex life-histories are common in nature, have many importantbiological consequences, and are an important focal area forintegrative biology. For organisms with complex life-histories,a legacy is something handed down from an ancestor or previousstage, and can be genetic, nutritional/provisional, experiential,as well as the result of random chance and natural variationin the environment. As we learn more about complex life-histories,it becomes clear that legacies are inexorably linked in theshort- and long-term through ecology and evolution. Understandingthe consequences and drivers of life-history patterns can thereforeonly be understood by considering all types of legacies andintegrating legacies across the entire life cycle. Larry McEdwardwas a leader in the field of ecological physiology, and evolutionaryecology of marine invertebrate larvae with complex life-histories.Through his scientific work and publications, devotion to students,colleagues, family, and friends, Larry has left a lasting legacythat will impact the future development of the field of larvalecology and complex life-histories.     

A life in books

A note from the Reviews Editor. How are intellectual and personal lives shaped through encounters with books? In this first instalment of our new occasional special feature, Tim Ingold responds to our invitation to select the five key books that most influenced his thinking. His selections illustrate the diversity of ways in which books can become meaningful: sometimes condensing an existing train of thought; in moments of inspiration; nagging away in languages that draw us in despite, or because of, the partial ways we understand; sometimes as points of arrival, redescribing what we already feel we know; at others as moments of departure, in new possibilities realized or glimpsed; in disagreement as much as assent; in the exhilarating revelation of a fleeting moment of discovery, or in the gradual evolution of new ideas over years or even decades.     

Artificial life and living systems: Insight into artificial life and its implications in life science research

Advanced technology has made it possible to build machines and systems like robots, which are capable of making intelligent decisions. Robots capable of self-replication and perform human functions are also available. The current challenge is to design evolutionary systems with high complexity comparable to that of biological networks. This is proposed to be achieved by ALife (Artificial Life). Here, we describe the promises provided by ALife for life sciences.     

Data clustering in life sciences

Clustering has a wide range of applications in life sciences and over the years has been used in many areas ranging from the analysis of clinical information, phylogeny, genomics, and proteomics. The primary goal of this article is to provide an overview of the various issues involved in clustering large biological datasets, describe the merits and underlying assumptions of some of the commonly used clustering approaches, and provide insights on how to cluster datasets arising in various areas within life sciences. We also provide a brief introduction to Cluto, a general purpose toolkit for clustering various datasets, with an emphasis on its applications to problems and analysis requirements within life sciences.     

Energy problems in life evolution

Evolutionary aspects of bioenergetics are considered. These include the origin of the first organisms, UV-protection and the beginnings of anoxygenic photosynthesis, the electron donor problem of life and the appearance of oxygenic photosynthesis, oxygen danger and strategies of defense, and the role of oxygen in programmed cell death.Translated from Biokhimiya, Vol. 70, No. 2, 2005, pp. 302–307.Original Russian Text Copyright © 2005 by Samuilov.This revised version was published online in April 2005 with corrections to the post codes.     

Loss in life expectancy and gain in life years as measures of cancer impact

There are a broad range of survival-based metrics that are available to report from cancer survival studies, with varying advantages and disadvantages. A combination of metrics should be considered to improve comprehensibility and give a fuller understanding of the impact of cancer. In this article, we discuss the utility of loss in life expectancy and gain in life years as measures of cancer impact, and to quantify differences across population groups. These measures are simple to interpret, have a real-world meaning, and evaluate impact over a life-time horizon. We illustrate the use of the loss in life expectancy measures through a range of examples using data on women diagnosed with cancer in England. We use four different examples across a number of tumour types to illustrate different uses of the metrics, and highlight how they can be interpreted and used in practice in population-based oncology studies. Extensions of the measures conditional on survival to specific times after diagnosis can be used to give updated prognosis for cancer patients. Furthermore, we show how the measures can be used to understand the impact of population differences seen across patient groups. We believe that these under-used, and relatively easy to calculate, measures of overall impact can supplement reporting of cancer survival metrics and improve the comprehensibility compared to the metrics typically reported.