Symbolic effects capitalized by nurses from the National Institute of Cancer in Brazil (1980 -1990)

ABSTRACT Objective: to describe the strategies of nurses in the National Institute of Cancer to disseminate its scientifi c capital and discuss the symbolic effects capitalized in the fi eld of oncology in the 1980s. Method: historical social studies, with primary sources consisting of written documents and oral reports, and as secondary sources, articles and books on the subject, based on the French sociologist Pierre Bourdieu's concepts of scientifi c capital and habitus. Results: it revealed the effective performance of nurses in this Institute on policies of cancer prevention and control and strategies used in the teaching of oncology nursing at the undergraduate level. In conclusion, nursing stands out in this context, through the dissemination of its scientifi c knowledge, as a participant in the construction of a scientifi c fi eld of oncology nursing in Brazil, highlighting the occupation of important social areas. Key words: Nursing; History of Nursing; Oncology Nursing. RESUMO Objetivo: descrever as estratégias dos enfermeiros do Instituto Nacional de Câncer para divulgação do seu capital científi co e discutir os efeitos simbólicos capitalizados no campo da oncologia na década de 1980. Método: estudo histórico-social, cujas fontes primárias constituíram-se de documentos escritos e depoimentos orais e, as secundárias, de artigos e livros sobre o tema, fundamentado com os conceitos de capital científi co e habitus do sociólogo francês Pierre Bourdieu. Resultados: evidenciou-se a efetiva atuação do enfermeiro desse Instituto nas políticas de prevenção e controle do câncer e das estratégias utilizadas no ensino de enfermagem em oncologia no curso de graduação. Concluiu-se que a Enfermagem destacou-se, nesse contexto, através da difusão do seu capital científi co, como participante da construção de um campo científi co da Enfermagem oncológica no Brasil, com destaque à ocupação de alguns espaços sociais importantes

Areas of natural occurrence of melipona scutellaris Latreille, 1811(Hymenoptera: Apidae) in the state of Bahia, Brazil.

The bee Melipona scutellaris is considered the reared meliponine species with the largest distribution in the North and Northeast regions of Brazil, with records from the state of Rio Grande do Norte down to the state of Bahia. Considering the importance of this species in the generation of income for family agriculture and in the preservation of areas with natural vegetation, this study aimed at providing knowledge on the distribution of natural colonies of M. scutellaris in the state of Bahia. Literature information, interviews with stinglessbee beekeepers, and expeditions were conducted to confirm the natural occurrence of the species. A total of 102 municipalities showed records for M. scutellaris, whose occurrence was observed in areas ranging from sea level up to 1,200-meter height. The occurrence of this species in the state of Bahia is considered to be restricted to municipalities on the coastal area and the Chapada Diamantina with its rainforests. Geographic coordinates, elevation, climate and vegetation data were obtained, which allowed a map to be prepared for the area of occurrence in order to support conservation and management policies for the species

From bit to it: How a complex metabolic network transforms information into living matter

Organisms live and die by the amount of information they acquire about their environment. The systems analysis of complex metabolic networks allows us to ask how such information translates into fitness. A metabolic network transforms nutrients into biomass. The better it uses information on available nutrient availability, the faster it will allow a cell to divide. I here use metabolic flux balance analysis to show that the accuracy I (in bits) with which a yeast cell can sense a limiting nutrient's availability relates logarithmically to fitness as indicated by biomass yield and cell division rate. For microbes like yeast, natural selection can resolve fitness differences of genetic variants smaller than 10-6, meaning that cells would need to estimate nutrient concentrations to very high accuracy (greater than 22 bits) to ensure optimal growth. I argue that such accuracies are not achievable in practice. Natural selection may thus face fundamental limitations in maximizing the information processing capacity of cells. The analysis of metabolic networks opens a door to understanding cellular biology from a quantitative, information-theoretic perspective