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1.
mBio ; 14(1): e0333522, 2023 02 28.
Artigo em Inglês | MEDLINE | ID: mdl-36723077

RESUMO

Climate change is a complex problem involving nonlinearities and feedback that operate across scales. No single discipline or way of thinking can effectively address the climate crisis. Teams of natural scientists, social scientists, engineers, economists, and policymakers must work together to understand, predict, and mitigate the rapidly accelerating impacts of climate change. Transdisciplinary approaches are urgently needed to address the role that microorganisms play in climate change. Here, we demonstrate with case studies how diverse teams and perspectives provide climate-change insight related to the range expansion of emerging fungal pathogens, technological solutions for harmful cyanobacterial blooms, and the prediction of disease-causing microorganisms and their vector populations using massive networks of monitoring stations. To serve as valuable members of a transdisciplinary climate research team, microbiologists must reach beyond the boundaries of their immediate areas of scientific expertise and engage in efforts to build open-minded teams aimed at scalable technologies and adoptable policies.


Assuntos
Mudança Climática , Políticas , Tecnologia
2.
Stat Med ; 27(23): 4779-89, 2008 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-18381707

RESUMO

Human immunodeficiency virus (HIV-1) can rapidly evolve due to selection pressures exerted by HIV-specific immune responses, antiviral agents, and to allow the virus to establish infection in different compartments in the body. Statistical models applied to HIV-1 sequence data can help to elucidate the nature of these selection pressures through comparisons of non-synonymous (or amino acid changing) and synonymous (or amino acid preserving) substitution rates. These models also need to take into account the non-independence of sequences due to their shared evolutionary history. We review how we have developed these methods and have applied them to characterize the evolution of HIV-1 in vivo. To illustrate our methods, we present an analysis of compartment-specific evolution of HIV-1 env in blood and cerebrospinal fluid and of site-to-site variation in the gag gene of subtype C HIV-1.


Assuntos
HIV-1/patogenicidade , Modelos Estatísticos , Filogenia , Seleção Genética , HIV-1/metabolismo , Humanos , Funções Verossimilhança , Produtos do Gene gag do Vírus da Imunodeficiência Humana/genética
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