Increasingly, says Jones, these zoonotic diseases are linked to environmental change and human behavior. The disruption of pristine forests driven by logging, mining, road building through remote places, rapid urbanization and population growth is bringing people into closer contact with animal species they may never have been near before, she says.
The resulting transmission of disease from wildlife to humans, she says, is now “a hidden cost of human economic development. There are just so many more of us, in every environment. We are going into largely undisturbed places and being exposed more and more. We are creating habitats where viruses are transmitted more easily, and then we are surprised that we have new ones.”

Source: ensia.com

Petter Törnberg and Justus Uitermark

Front. Sustain. Cities

 

Digital platforms are reshaping cities in the twenty-first century, providing not only new ways of seeing and navigating the world, but also new ways of organizing the economy, our cities and social lives. They bring great promises, claiming to facilitate a new “sharing” economy, outside of the exploitation of the market and the inefficiencies of the state. This paper reflects on this promise, and its associated notion of “self-organization,” by situating digital platforms in a longer history of control, discipline and surveillance. Using Foucault, Deleuze, and Bauman, we scrutinize the theoretical and political notion of “self-organization” and unpack its idealistic connotations: To what extent does self-organization actually imply empowerment or freedom? Who is the “self” in “self-organization,” and who is the user on urban digital platforms? Is self-organization necessarily an expression of the interests of the constituent participants? In this way, the paper broadens the analysis of neoliberal governmentalities to reveal the forms of power concealed under the narratives of “sharing” and “self-organization” of the platform era. We find that control is increasingly moving to lower-level strata, operating by setting the context and conditions for self-organization. Thus, the order of things emerge seemingly naturally from the rules of the game. This points to an emerging form of complex control, which has gone beyond the fast and flexible forms of digital control theorized by Deleuze.

Source: www.frontiersin.org

Sergey Mityagin, Carlos Gershenson, and Alexander Boukhanovsky

Complexity Volume 2020 |Article ID 9135024

 

In this paper, we present an approach to drug addiction simulation and forecasting in the medium and long terms in cities having a high population density and a high rate of social communication. Drug addiction forecasting is one of the basic components of the antidrug policy, giving informational and analytic support both at the regional and at the governmental level. However, views on the drug consumption problem vary in different regions, and as a consequence, several approaches to antidrug policy implementation exist. Thereby, notwithstanding the fact that the phenomenology of the population narcotization process is similar in the different regions, approaches to the modeling of drug addiction may also substantially differ for different kinds of antidrug policies. This paper presents a survey of the available antidrug policies and the corresponding approaches to the simulation of population narcotization. This article considers the approach to the construction of the regression model of anesthesia on the main components formed on the basis of indicators of social and economic development. The substantiation of the chosen method is given, which is associated with a significant correlation of indicators, which characterizes the presence of a small number of superfactors. This allows us to form a conclusion about the general level of development of the region as the main factor determining the drug addiction. A new model is proposed for one of the most widespread antidrug policies, namely, the drug use reduction policy. The model helps determine the significant factors of population narcotization and allows to estimate its damage. The model is tested successfully using St. Petersburg data.

Source: www.hindawi.com

Joana C. Xavier, Wim Hordijk, Stuart Kauffman, Mike Steel and William F. Martin

Proceedings of the Royal Society B: Biological Sciences

 

Modern cells embody metabolic networks containing thousands of elements and form autocatalytic sets of molecules that produce copies of themselves. How the first self-sustaining metabolic networks arose at life’s origin is a major open question. Autocatalytic sets smaller than metabolic networks were proposed as transitory intermediates at the origin of life, but evidence for their role in prebiotic evolution is lacking. Here, we identify reflexively autocatalytic food-generated networks (RAFs)—self-sustaining networks that collectively catalyse all their reactions—embedded within microbial metabolism. RAFs in the metabolism of ancient anaerobic autotrophs that live from H2 and CO2 provided with small-molecule catalysts generate acetyl-CoA as well as amino acids and bases, the monomeric components of protein and RNA, but amino acids and bases without organic catalysts do not generate metabolic RAFs. This suggests that RAFs identify attributes of biochemical origins conserved in metabolic networks. RAFs are consistent with an autotrophic origin of metabolism and furthermore indicate that autocatalytic chemical networks preceded proteins and RNA in evolution. RAFs uncover intermediate stages in the emergence of metabolic networks, narrowing the gaps between early Earth chemistry and life.

Source: royalsocietypublishing.org

Ruiyun Li, Sen Pei, Bin Chen, Yimeng Song, Tao Zhang, Wan Yang, Jeffrey Shaman

Science 16 Mar 2020:
eabb3221
DOI: 10.1126/science.abb3221

 

Estimation of the prevalence and contagiousness of undocumented novel coronavirus (SARS-CoV2) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV2, including the fraction of undocumented infections and their contagiousness. We estimate 86% of all infections were undocumented (95% CI: [82%–90%]) prior to 23 January 2020 travel restrictions. Per person, the transmission rate of undocumented infections was 55% of documented infections ([46%–62%]), yet, due to their greater numbers, undocumented infections were the infection source for 79% of documented cases. These findings explain the rapid geographic spread of SARS-CoV2 and indicate containment of this virus will be particularly challenging.

Source: science.sciencemag.org