Hyobin Kim, Stalin Muñoz, Pamela Osuna, Carlos Gershenson

 

Robustness and evolvability are essential properties to the evolution of biological networks. To determine if a biological network is robust and/or evolvable, the comparison of its functions before and after mutations is required. However, it has an increasing computational cost as network size grows. Here we aim to develop a predictor to estimate the robustness and evolvability of biological networks without an explicit comparison of functions. We measure antifragility in Boolean network models of biological systems and use this as the predictor. Antifragility is a property to improve the capability of a system through external perturbations. By means of the differences of antifragility between the original and mutated biological networks, we train a convolutional neural network (CNN) and test it to classify the properties of robustness and evolvability. We found that our CNN model successfully classified the properties. Thus, we conclude that our antifragility measure can be used as a significant predictor of the robustness and evolvability of biological networks.

Source: arxiv.org

Mario Villalobos, Pablo Razeto-Barry

Adaptive Behavior Vol 28, Issue 1, 2020

 

Building on the original formulation of the autopoietic theory (AT), extended enactivism argues that living beings are autopoietic systems that extend beyond the spatial boundaries of the organism. In this article, we argue that extended enactivism, despite having some basis in AT’s original formulation, mistakes AT’s definition of living beings as autopoietic entities. We offer, as a reply to this interpretation, a more embodied reformulation of autopoiesis, which we think is necessary to counterbalance the (excessively) disembodied spirit of AT’s original formulation. The article aims to clarify and correct what we take to be a misinterpretation of AT as a research program. AT, contrary to what some enactivists seem to believe, did not (and does not) intend to motivate an extended conception of living beings. AT’s primary purpose, we argue, was (and is) to provide a universal individuation criterion for living beings, these understood as discrete bodies that are embedded in, but not constituted by, the environment that surrounds them. However, by giving a more explicitly embodied definition of living beings, AT can rectify and accommodate, so we argue, the enactive extended interpretation of autopoiesis, showing that although living beings do not extend beyond their boundaries as autopoietic unities, they do form part, in normal conditions, of broader autopoietic systems that include the environment.

Source: journals.sagepub.com

This is a Target Article. See Also: Opinions and Reply

Rion Brattig Correia, Ian B. Wood, Johan Bollen, Luis M. Rocha

 

Social media data has been increasingly used to study biomedical and health-related phenomena. From cohort level discussions of a condition to planetary level analyses of sentiment, social media has provided scientists with unprecedented amounts of data to study human behavior and response associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance, sentiment analysis especially for mental health, and other areas. We also discuss a variety of innovative uses of social media data for health-related applications and important limitations in social media data access and use.

Source: arxiv.org

Kaiyuan Sun, Jenny Chen, Cécile Viboud.

 

Starting in December 2019, Chinese health authorities have been closely monitoring a cluster of pneumonia cases in the city of Wuhan, in Hubei Province. It has been determined that the causing agent of the viral pneumonia among affected individuals is a new coronavirus (2019-nCoV). As of January 29, 2020, a total of 6,088 cases have been detected and confirmed in Mainland China, with more than 70 additional cases detected and confirmed internationally in Japan, Thailand, South Korea, Taiwan, Singapore, Vietnam, United States, France, Australia, Nepal, Canada, Cambodia, Sri Lanka, United Arab Emirates, Finland, and Germany. By using the cases detected outside China we are providing estimates of size of the Wuhan outbreak as of January 29th, 2020.​ By using an estimate of 10 days from exposure to detection and an effective population of 20 million people in Wuhan catchment area the estimated median size of the Wuhan outbreak is 31,200 infections [95% CI: 23,400-40,400]. Technical details are in the full report available below. 

Source: www.mobs-lab.org

Davis, Jessica, Perra, Nicola, Zhang, Qian, Moreno, Yamir and Vespignani, Alessandro (2020) Phase transitions in information spreading on structured populations. Nature Physics. ISSN 1745-2473 (Print), 1745-2481 (Online) (In Press)

 

Mathematical models of social contagion that incorporate networks of human interactions have become increasingly popular, however, very few approaches have tackled the challenges of including complex and realistic properties of socio-technical systems. In this work we define a framework to characterize the dynamics of the Maki-Thompson rumor spreading model in structured populations, and analytically find a previously uncharacterized dynamical phase transition that separates the local and global contagion regimes. We validate our threshold prediction through extensive Monte Carlo simulations. Furthermore, we apply this framework in two real-world systems, the European commuting and transportation network and the Digital Bibliography and Library Project (DBLP) collaboration network. Our findings highlight the importance of the underlying population structure in understanding social contagion phenomena and have the potential to define new intervention strategies aimed at hindering or facilitating the diffusion of information in socio-technical systems.

Source: gala.gre.ac.uk