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Register a new userA Cross-disciplinary Framework for the Description of Contextually Mediated Change
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We present a mathematical framework (referred to as Context-driven Actualization of Potential, or CAP) for describing how entities change over time under the influence of a context. The approach facilitates comparison of change of state of entities studied in different disciplines. Processes are seen to differ according to the degree of nondeterminism, and the degree to which they are sensitive to, internalize, and depend upon a particular context. Our analysis suggests that the dynamical evolution of a quantum entity described by the Schrodinger equation is not fundamentally different from change provoked by a measurement often referred to as collapse, but a limiting case, with only one way to collapse. The biological transition to coded replication is seen as a means of preserving structure in the fact of context-driven change, and sextual replication as a means of increasing potentiality thus enhancing diversity through interaction with context. The framework sheds light on concepts like selection and fitness, reveals how exceptional Darwinian evolution is as a means of change of state, and clarifies in what sense culture, and the creative process underlying it, are Darwinian.
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The body is home to a diverse microbiota, mainly in the gut. Resistant bacteria are selected for by antibiotic treatments, and once resistance becomes widespread in a population of hosts, antibiotics become useless. Here, we develop a multiscale model of the interaction between antibiotic use and resistance spread in a host population, focusing on an important aspect of within-host immunity. Antibodies secreted in the gut enchain bacteria upon division, yielding clonal clusters of bacteria. We demonstrate that immunity-driven bacteria clustering can hinder the spread of a novel resistant bacterial strain in a host population. We quantify this effect both in the case where resistance pre-exists and in the case where acquiring a new resistance mutation is necessary for the bacteria to spread. We further show that the reduction of spread by clustering can be countered when immune hosts are silent carriers, and are less likely to get treated, and/or have more contacts. We demonstrate the robustness of our findings to including stochastic within-host bacterial growth, a fitness cost of resistance, and its compensation. Our results highlight the importance of interactions between immunity and the spread of antibiotic resistance, and argue in the favor of vaccine-based strategies to combat antibiotic resistance.
MHD-based global space weather models have mostly been developed and maintained at academic institutions. While the free spirit approach of academia enables the rapid emergence and testing of new ideas and methods, the lack of long-term stability and support makes this arrangement very challenging. This paper describes a successful example of a university-based group, the Center of Space Environment Modeling (CSEM) at the University of Michigan, that developed and maintained the Space Weather Modeling Framework (SWMF) and its core element, the BATS-R-US extended MHD code. It took a quarter of a century to develop this capability and reach its present level of maturity that makes it suitable for research use by the space physics community through the Community Coordinated Modeling Center (CCMC) as well as operational use by the NOAA Space Weather Prediction Center (SWPC).
A hidden variables model complying with the simplest form of Local Realism was recently introduced, which reproduces Quantum Mechanics predictions for an even ideally perfect Bells experiment. This is possible thanks to the use of a non-Boolean vector hidden variable. Yet, that model is as far as Quantum Mechanics from the goal of providing a complete description of physical reality in the EPR-sense. Such complete description includes the capacity to calculate, from the values taken by the hidden variables, the time values when particles are detected. This can be achieved by replacing Borns rule (which allow calculating only probabilities) with a deterministic condition for particle detection. The simplest choice is a threshold condition on the hidden variables. However, in order to test this choice, a new type of quantum (or wave, or non-Boolean) computer is necessary. This new type of quantum computer does not exist yet, not even in theory. In this paper, a classical (Boolean) computer code is presented which mimics the operation of that new type of quantum computer by using contextual instructions. These instructions take into account a consequence of the principle of superposition (which is a typical vector, i.e. non-Boolean, feature). Numerical results generated by the mimicking code are analyzed. They illustrate the features the hypothetical new type of quantum computers output may have, and show how and why some intuitive assumptions about Bells experiment fail.
In epidemiological modelling, the instantaneous reproduction number, $R_t$, is important to understand the transmission dynamics of infectious diseases. Current $R_t$ estimates often suffer from problems such as lagging, averaging and uncertainties demoting the usefulness of $R_t$. To address these problems, we propose a new method in the framework of sequential Bayesian inference where a Data Assimilation approach is taken for $R_t$ estimation, resulting in the state-of-the-art DAR$_t$ system for $R_t$ estimation. With DAR$_t$, the problem of time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and $R_t$; the drawback of averaging is improved by instantaneous updating upon new observations and a model selection mechanism capturing abrupt changes caused by interventions; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DAR$_t$ through simulations and demonstrate its power in revealing the transmission dynamics of COVID-19.
We study the coevolutionary dynamics of the diversity of phenotype expression and the evolution of cooperation in the Prisoners Dilemma game. Rather than pre-assigning zero-or-one interaction rate, we diversify the rate of interaction by associating it with the phenotypes shared in common. Individuals each carry a set of potentially expressible phenotypes and expresses a certain number of phenotypes at a cost proportional to the number. The number of expressed phenotypes and thus the rate of interaction is an evolvable trait. Our results show that nonnegligible cost of expressing phenotypes restrains phenotype expression, and the evolutionary race mainly proceeds on between cooperative strains and defective strains who express a very few phenotypes. It pays for cooperative strains to express a very few phenotypes. Though such a low level of expression weakens reciprocity between cooperative strains, it decelerates rate of interaction between cooperative strains and defective strains to a larger degree, leading to the predominance of cooperative strains over defective strains. We also find that evolved diversity of phenotype expression can occasionally destabilize due to the invasion of defective mutants, implying that cooperation and diversity of phenotype expression can mutually reinforce each other. Therefore, our results provide new insights into better understanding the coevolution of cooperation and the diversity of phenotype expression.
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