59

Yes, this helps as well with other infectious diseases. A good example is the flu, which season was measurably shorter this year than in other years on record. See the figure from the reference 1 for comparision: Reference 2 shows that this is also true for other respiratory diseases (figure 2): This shows very well that the isolation measures and the ...


40

In addition to Chris' answer above, the effect is even more pronounced in Southern Hemisphere countries where flu season started during the pandemic. The New Zealand lockdown and health response dramatically lowered the prevalence of reported flu-like symptoms. Reference: Flu Tracking reports - New Zealand – week ending 31-May-2020


25

This virology site has a post about a 2017 paper about membrane-vesicled plasmids that act in ways that are theorized to be precursors to how viruses work: It is likely that the plasmid-containing membrane vesicles are precursors of what we know today as virus particles. It is thought that viruses originated from selfish genetic elements such as plasmids ...


14

There are giant viruses that some people think could be degenerate bacteria. https://en.wikipedia.org/wiki/Mimivirus Mimivirus shows many characteristics which place it at the boundary between living and non-living. It is as large as several bacterial species, such as Rickettsia conorii and Tropheryma whipplei, possesses a genomic size comparable ...


5

I think that the simple answer to this question is that the present comparative methodology was largely established by Felsenstein 1985, American Naturalist. For mathematical convenience, he suggested Brownian motion as a null hypothesis, because "...the variance of the distribution of change of a branch is proportional to the length of time of the ...


3

My question concerns the way that $d$ enters the SIR model, because I find it not so plausible: to consider all persons that are infected today and take a fraction $ν$ of them that will have recovered tomorrow. Well, it is in fact not very 'realistic' as you point out, but in the assumptions of the model, we see that the population has no ...


3

I mean, I question if there could species that chose from their own gens, those who had been more useful during their life and increasing the chance that their descendants had them since their conception. There are two kinds of argument against this - one from basic principles, and one from empirical evidence. As far as basic principles, I think Dawkins ...


3

Cell division happens by division at the middle of the rod, so the result is two daughter cells that are at nearly the same angle. Over time in the absence of agitation moving cells around, this will lead to a groups of cells that have non-independent cell orientations. For more information, including how people have induced it to do otherwise, see this ...


3

In your second step $(K_d)^n$ should be $(K_A)^n$. Where: $K_d$ is the apparent dissociation constant and $K_{A}$ is the ligand concentration that results in half occupation See for example the relevant wikipedia article.


2

This is probably not a very satisfactory answer. The very next step in Murray's book (3rd Ed) there is a non-dimensionalised version of the model (eq 3.32) which looks like the one given in Boyce's book (10th Ed) except $\epsilon_i$ and $\sigma_i$ (eq 2, Ch 9). If you plug in $\epsilon_i = \sigma_i = 1$ in Boyce's book you get the same fixed point expression ...


2

I think your issue is mainly with the interpretation of the isoclines. If you assume this standard predator-prey model: $$\frac{dN_1}{dt} = rN_1-pN_1N_2$$ $$\frac{dN_2}{dt} = apN_1N_2-mN_2$$ you get the isoclines: $$ N_2 = \frac{r}{p} $$ $$ N_1 = \frac{m}{ap}$$ with the first isocline referring to prey and the second to predators. These isoclines mean ...


2

$$R_0 ∝ \left(\frac{\text{infection}}{\text{contact}}\right) · \left(\frac{\text{contact}}{\text{time}}\right) · \left(\frac{\text{time}}{\text{infection}}\right)$$ More specifically: $$R_0 = τ · \bar{c} · d \quad \quad (1)$$ where $τ$ is the transmissibility (i.e., probability of infection given contact between a susceptible and infected ...


2

I know this is too late, but since this is something I'm struggling with too I thought I'd post here in case others also find there way here. I don't have an answer but, here are some quotes that might help (I found them at least partially helpful). Yang (2004): "Marginal reconstruction is more suitable when one wants the sequence at a particular node, as ...


2

The randomness of the assortment of genes was first shown by the experiments of Mendel in his experiments with pea plants, and demonstrated numerous times in different contexts in classical genetics. There are specific instances where the assortment is not perfectly random. This is called meiotic drive. There are some genes that will skew the meiotic ...


2

It's unclear how the laws of thermodynamics relate to the dangers of UV light in your question, but if I understand correctly your reasoning is the following: 1) The laws of thermodynamics say that order cannot increase in an isolated system, precluding life. 2) Life exists anyway because the Earth is not an isolated system: it receives energy from the Sun ...


2

Let $\theta = 4n\mu$, then $E(S) = \theta\sum\limits_{i=1}^{k-1}\frac{1}{i}$, assuming infinite sites model. This is indeed given by Watterson (1975).


1

Just came across a chapter by Felsenstein on phylogenetic inference with quantitative characters. In it, he states this biological justification for using Brownian motion: A quantitative trait that has genetic variation controlled by a single locus will change as the gene frequencies at the locus undergo genetic drift... Brownian motion is a reasonable ...


1

It's funny: $R_0$ doesn't actually follow as nicely as $1/R_0$ when focusing on the infected class. In a SIS model: $$\frac{\mathrm{d}S}{\mathrm{d}t} = -\beta SI + \alpha I \\ \frac{\mathrm{d}I}{\mathrm{d}t} = \beta SI - \alpha I$$ take finding the steady state of $I$. Whether a disease is able to invade is dependent on whether or not the steady state is ...


1

1/R0 is the threshold fraction. If fraction of population vulnerable to particular infection is more than 1/R0, only then infection can spread further. And if it is less than 1/R0 then infection can not progress and eventually goes away And, 1-1/R0 is the fraction of population which requires vaccination so that we can have herd immunity. (Reference http:/...


1

I am not sure if I quite understand your question, but I think your problem is here: removal (and your d) is a rate (time/removal). It does not matter what time you choose; a day, a week, a year, as long as you adjust your c (which is /time) to same timescale. In other words, if you wish to use d over several days, you need to calculate your contacts over ...


1

Bacterial cells prefer to attach with each other in parallel orientation so that they have maximum surface of contact and could form biofilm clusters. Both physical factors (like Brownian motion, electrostatic interactions, gravity, van der Waals forces and hydrodynamics) and cellular function in bacterial cells (bacterial motility, production of ...


1

I ended up writing directly to Joe after not getting an outside answer here. I won't directly quote him without his permission, but to sum up, he wrote the following: This paper (among others) gives an numerical treatment of different selection coefficients in small populations, which effectively means loci that are not under selection but still have ...


1

You can write these equations in the form of ODEs but it is not really essential. This is my analysis (just the model formulation and not literature): The effects of interactor species may or may not be dependent on the population of the species that is being analysed (recipient). Now you have to model based on how you would realistically expect the ...


1

Volterra’s Variations and Fluctuations of the Number of Individuals in Animal Species living together features what is nowadays often called the generalised Lotka–Volterra model. Thus it captures competition between and with populations. In particular, Section 2 is titled: Biological Association of two Species which contend for the same Food.


1

I imagine that for case 1 you have data of the type: Plant1, $t_f$, $t_l$; Plant2, $t_f$, $t_l$; etc. If you are interested in comparing the mean temperatures between flowers and leaves, your data would consist of n measurements coming from flowers and n measurements from leaves. You may surely try a t-test for independent samples, especially if you have ...


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