Human extinction

Summary

Human extinction is the hypothetical end of the human species due to either natural causes such as population decline from sub-replacement fertility, an asteroid impact, large-scale volcanism, or via anthropogenic destruction (self-extinction). For the latter, some of the many possible contributors include climate change, global nuclear annihilation, biological warfare, and ecological collapse. Other scenarios center on emerging technologies, such as advanced artificial intelligence, biotechnology, or self-replicating nanobots. The scientific consensus is that there is a relatively low risk of near-term human extinction due to natural causes. The likelihood of human extinction through humankind's own activities, however, is a current area of research and debate. History of thought Early history of thinking about human extinction Before the 18th and 19th centuries, the possibility that humans or other organisms could become extinct was viewed with scepticis

Official source

https://en.wikipedia.org/wiki/Human_extinction

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Adaptive Task-Space Force Control for Humanoid-to-Human Assistance

Anastasia Bolotnikova

We envision a humanoid robot to serve as a source of additional motion-support forces in assistance for frail persons. In this context, we present a control strategy for a humanoid to adaptively regulate its assistive force contribution. First, we identify a human model torque control for optimal execution of a priori known motion task from sample recordings of this task performed by a healthy individual. We utilize the identified model in the proposed position discrepancy based observer of the human torque contribution, the unknown and unmeasurable variable. We propose an experience-based human contribution model learning strategy that allows to improve the human contribution estimate from trial-to-trial. The target assistive torque contribution is then calculated as the difference between the optimal torque required for the motion task and the estimated human contribution. The target assistive torque is integrated into a multi-robot quadratic programming task-space controller to compute the desired interaction force required for the robot to supply the necessary assistive torque for the human model. We use the non-optimal recordings of the human motion task to emulate human frailty and apply our adaptive force control strategy to demonstrate the results of a humanoid successfully assisting the simulated human model to restore the optimal motion task performance.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

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We study the extinction time tau of the contact process started with full occupancy on finite trees of bounded degree. We show that, if the infection rate is larger than the critical rate for the contact process on Z, then, uniformly over all trees of degree bounded by a given number, the expectation of tau grows exponentially with the number of vertices. Additionally, for any increasing sequence of trees of bounded degree, t divided by its expectation converges in distribution to the unitary exponential distribution. These results also hold if one considers a sequence of graphs having spanning trees with uniformly bounded degree, and provide the basis for powerful coarse-graining arguments. To demonstrate this, we consider the contact process on a random graph with vertex degrees following a power law. Improving a result of Chatterjee and Durrett (2009), we show that, for any non-zero infection rate, the extinction time for the contact process on this graph grows exponentially with the number of vertices. (C) 2016 Elsevier B.V. All rights reserved.

Elsevier Science Bv2016

An experimental evaluation of drug-induced mutational meltdown as an antiviral treatment strategy

Claudia Bank, Matthieu Foll, Jeffrey David Jensen, Sebastian Matuszewski, Hyunjin Shim

The rapid evolution of drug resistance remains a critical public health concern. The treatment of influenza A virus (IAV) has proven particularly challenging, due to the ability of the virus to develop resistance against current antivirals and vaccines. Here, we evaluate a novel antiviral drug therapy, favipiravir, for which the mechanism of action in IAV involves an interaction with the viral RNA-dependent RNA polymerase resulting in an effective increase in the viral mutation rate. We used an experimental evolution framework, combined with novel population genetic method development for inference from time-sampled data, to evaluate the effectiveness of favipiravir against IAV. Evaluating whole genome polymorphism data across 15 time points under multiple drug concentrations and in controls, we present the first evidence for the ability of IAV populations to effectively adapt to low concentrations of favipiravir. In contrast, under high concentrations, we observe population extinction, indicative of mutational meltdown. We discuss the observed dynamics with respect to the evolutionary forces at play and emphasize the utility of evolutionary theory to inform drug development.

Wiley-Blackwell2016