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Register a new userMissing and spurious interaction in additive, multiplicative and odds ratio models
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Added by
Jorge Fernandez-de-Cossio
Publication date
2017
fields
Biology
and research's language is
English
Authors
Jorge Fernandez-de-Cossio
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Additive, multiplicative, and odd ratio neutral models for interactions are for long advocated and controversial in epidemiology. We show here that these commonly advocated models are biased, leading to spurious interactions, and missing true interactions.
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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus caused the novel coronavirus disease-2019 (COVID-19) affecting the whole world. Like SARS-CoV and MERS-CoV, SARS-CoV-2 are thought to originate in bats and then spread to humans through intermediate hosts. Identifying intermediate host species is critical to understanding the evolution and transmission mechanisms of COVID-19. However, determining which animals are intermediate hosts remains a key challenge. Virus host-genome similarity (HGS) is an important factor that reflects the adaptability of virus to host. SARS-CoV-2 may retain beneficial mutations to increase HGS and evade the host immune system. This study investigated the HGSs between 399 SARS-CoV-2 strains and 10 hosts of different species, including bat, mouse, cat, swine, snake, dog, pangolin, chicken, human and monkey. The results showed that the HGS between SARS-CoV-2 and bat was the highest, followed by mouse and cat. Human and monkey had the lowest HGS values. In terms of genetic similarity, mouse and monkey are halfway between bat and human. Moreover, given that COVID-19 outbreaks tend to be associated with live poultry and seafood markets, mouse and cat are more likely sources of infection in these places. However, more experimental data are needed to confirm whether mouse and cat are true intermediate hosts. These findings suggest that animals closely related to human life, especially those with high HGS, need to be closely monitored.
We study sets of recurrence, in both measurable and topological settings, for actions of $(mathbb{N},times)$ and $(mathbb{Q}^{>0},times)$. In particular, we show that autocorrelation sequences of positive functions arising from multiplicative systems have positive additive averages. We also give criteria for when sets of the form ${(an+b)^{ell}/(cn+d)^{ell}: n in mathbb{N}}$ are sets of multiplicative recurrence, and consequently we recover two recent results in number theory regarding completely multiplicative functions and the Omega function.
The concept of biological adaptation was closely connected to some mathematical, engineering and physical ideas from the very beginning. Cannon in his The wisdom of the body (1932) used the engineering vision of regulation. In 1938, Selye enriched this approach by the notion of adaptation energy. This term causes much debate when one takes it literally, i.e. as a sort of energy. Selye did not use the language of mathematics, but the formalization of his phenomenological theory in the spirit of thermodynamics was simple and led to verifiable predictions. In 1980s, the dynamics of correlation and variance in systems under adaptation to a load of environmental factors were studied and the universal effect in ensembles of systems under a load of similar factors was discovered: in a crisis, as a rule, even before the onset of obvious symptoms of stress, the correlation increases together with variance (and volatility). During 30 years, this effect has been supported by many observations of groups of humans, mice, trees, grassy plants, and on financial time series. In the last ten years, these results were supplemented by many new experiments, from gene networks in cardiology and oncology to dynamics of depression and clinical psychotherapy. Several systems of models were developed: the thermodynamic-like theory of adaptation of ensembles and several families of models of individual adaptation. Historically, the first group of models was based on Selyes concept of adaptation energy and used fitness estimates. Two other groups of models are based on the idea of hidden attractor bifurcation and on the advection--diffusion model for distribution of population in the space of physiological attributes. We explore this world of models and experiments, starting with classic works, with particular attention to the results of the last ten years and open questions.
Recent studies have shown that the majority of published computational models in systems biology and physiology are not repeatable or reproducible. There are a variety of reasons for this. One of the most likely reasons is that given how busy modern researchers are and the fact that no credit is given to authors for publishing repeatable work, it is inevitable that this will be the case. The situation can only be rectified when government agencies, universities and other research institutions change policies and that journals begin to insist that published work is in fact at least repeatable if not reproducible. In this chapter guidelines are described that can be used by researchers to help make sure their work is repeatable. A scoring system is suggested that authors can use to determine how well they are doing.
We give a proof-theoretic and algorithmic complexity analysis for systems introduced by Morrill to serve as the core of the CatLog categorial grammar parser. We consider two rece
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