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Verb Phrases wait on , to perform the duties of an attendant or servant for. Chiefly Midland and Southern U. Also wait upon. Origin of wait —; v. Wait, tarry imply pausing to linger and thereby putting off further activity until later. Wait usually implies staying for a limited time and for a definite purpose, that is, for something expected: to wait for a train.
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Tarry is a somewhat archaic word for wait, but it suggests lingering, perhaps aimlessly delaying, or pausing briefly in a journey: to tarry on the way home; to tarry overnight at an inn. Can be confused wait weight. Usage note 15e, f.tornado.burnsforce.com/mercedes-benz-a-bordo-manual-de-telfono.php
Why does camera uploads show a “Waiting to upload” error?
Wait on or upon an event does not have a regional pattern and occurs in a wide variety of contexts: We will wait on or upon his answer and make our decision then. The completion of the merger waits upon news of a drop in interest rates. Examples from the Web for waiting Should lightning strike and Hillary Clinton forgoes a presidential run, Democrats have a nominee in waiting.
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If you have to stop on the roadside: do not park facing against the traffic flow stop as close as you can to the side do not stop too close to a vehicle displaying a Blue Badge: remember, the occupant may need more room to get in or out you MUST switch off the engine, headlights and fog lights you MUST apply the handbrake before leaving the vehicle you MUST ensure you do not hit anyone when you open your door.
Check for cyclists or other traffic it is safer for your passengers especially children to get out of the vehicle on the side next to the kerb put all valuables out of sight and make sure your vehicle is secure lock your vehicle. Any vehicle may enter a bus lane to stop, load or unload where this is not prohibited see Rule Rule Goods vehicles. Rule Parking on hills. Rule Turn your wheels away from the kerb when parking facing uphill. Turn them towards the kerb when parking facing downhill Decriminalised Parking Enforcement DPE DPE is becoming increasingly common as more authorities take on this role.
Print this page. Is this page useful? These differences are nicely summarized in Figure 2B of L udwig et al. The assumptions for intermediate population size as compared to mutation rates 1 hold and, as predicted by Theorem 1, the waiting time is a good fit to the exponential distribution. In a simulation study, S tone and W ray estimated the rate of de novo generation of regulatory sequences from a random genetic background.
However, as M ac A rthur and B rookfield have already pointed out, there is a serious problem with Stone and Wray's computation. They assumed individuals in the population evolve independently, while in reality there are significant correlations due to their common ancestors. Motivated by this simulation study, D urrett and S chmidt have recently given a mathematical analysis for regulatory sequence evolution in humans, correcting the calculation mentioned above. The authors defined this as the nucleotide at the site if there is no variability in the population and if the site is variable, the most frequent nucleotide at that site in the population.
Using a generation time of 25 years, they found that in a 1-kb region, the average waiting time for words of length six was , years. Fortunately, in biological reality, the match of a regulatory protein to the target sequence does not have to be exact for binding to occur. Biological reality is complicated, with the acceptable sequences for binding described by position weight matrices that indicate the flexibility at different points in the sequence. To simplify, we assume that binding will occur to any eight-letter word that has seven letters in common with the target word.
To explain the intuition behind the last result, consider the case of eight-letter words.
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Simple calculations then show that the waiting time to improve one of these six of eight matches to seven of eight has a mean of 60, years. This shows that new regulatory sequences can come from small modifications of existing sequence. Extending our previous work on the de novo generation of binding sites, this article considers the possibility that in a short amount of time, two changes will occur, the first of which inactivates an existing binding site, and the second of which creates a new one.
This problem was studied earlier by C arter and W agner In the next section, we present the model and then a simpler theoretical analysis based on work of K omarova et al. Finally, we compare the theory with simulations and experimental results. Consider a population of 2 N haploid individuals. The reader should think of this as the chromosomes of N diploid individuals evolving under the assumptions of random union of gametes and additive fitness.
However, since we use the continuous-time Moran model, it is simpler and clearer to state our results for haploid individuals. We start with a homogeneous population of wild-type individuals. We have two sets of possible mutant genotypes A and B. Wild-type individuals mutate to type A at rate u 1 and type A individuals mutate to type B at rate u 2. We assume there is no back mutation. We think of the A mutation as damaging an existing transcription factor binding site and the B mutation as creating a second new binding site at a different location within the regulatory region.
We assign relative fitnesses 1, r , and s to wild-type, A mutant, and B mutant individuals, respectively. See Figure 1 for a diagram of our model. We used the word damage above to indicate that the mutation may only reduce the binding efficiency, not destroy the binding site.
However, even if it does, the mutation need not be lethal. In most cases the B mutation will occur when the number of A mutants is a small fraction of the population, so most individuals with the A mutation will also carry a working copy of the binding site. An example of our general two-stage mutation process used in this article is as follows. The regulatory region contains two possible binding sites, a and b, where a prime denotes an inactivated site.
The relative fitnesses of wild type, A mutant, and B mutant are 1, r , and s , respectively. Note that in this case, wild-type individuals cannot produce individuals with a second active binding site. For a different example of this general process, see p. We could also assume that the mutations occur in the other order: B first and then A. This is also a two-stage process that falls into the general framework of our analysis below under the appropriate fitness assumptions.
The problems in population genetics to be solved are as follows: How long do we have to wait i for a type B mutation to arise in some individual or ii for the B mutant to become fixed in the population? These problems were investigated by K omarova et al. A nice account of these results can be found in Section Here, we apply these results to estimate the waiting time for a switch between two transcription factor binding sites, as defined in the statement of our problem above. First we need to describe the population genetics model we are considering.
Rather than use the discrete-time Wright—Fisher model, we use the continuous-time Moran model.
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We prefer the Moran process because it is a birth-and-death chain, which means that the number of type A individuals increases or decreases by one on each event. Biologically the Moran model corresponds to a population with overlapping generations and in the case of tumor suppressor genes is appropriate for a collection of cells in an organ that is being maintained at a constant size.
As the reader will see from the definition, the Moran process as a genetic model treats N diploids as 2 N haploids and replaces one chromosome at a time. In this context it is common to invoke random union of gametes and assume fitnesses are additive, but that is not necessary. Since homozygous mutants are rare, the fitness of an A mutant is its fitness in the heterozygous state.
Supposing that the relative fitnesses have been normalized to have maximum value 1, the dynamics may be described as follows:. Mutation changes the copy from wild type to A with probability u 1 and from A to B with probability u 2. Otherwise, nothing happens. For more on this model, see Section 3. T heorem 1. If and , the probability P t that a B mutation has occurred in some member of the population by time t is. The last conclusion in the theorem should be intuitive since successful mutations i.
Sketch of proof. The mathematical proof of this result involves some technical complications, but the underlying ideas are simple. Here and in what follows, readers not interested in the underlying theory can skip the proof sketches.
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A simple calculation, see Section 2 of I wasa et al. Since B mutants have probability u 2 , there is a reasonable chance of having a B mutation before the number of A mutants returns to 0. I wasa et al.
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C arter and W agner had earlier noted this possibility but they did not end up with a very nice formula for the average fixation time; see their 2. The assumption implies that throughout the scenario we have just described, the number of type A mutants is a small fraction of the population, so we can ignore the probability that the A mutants become fixed in the population.
In contrast to populations of intermediate size, populations that are small compared to the mutation rates have fixation of the type A mutation before the type B mutation arises. T heorem 2. If and , then the probability P t that a B mutation has occurred in some member of the population by time t is. The condition implies that it is unlikely for the B mutation to appear before A reaches fixation. When 2 N and are about the same size, fixation of an A mutation and stochastic tunneling are both possible situations in which a type B mutation can arise, and the analysis becomes very complicated.
Recent work of D urrett et al. T heorem 3. T heorem 4. The probability P t that the B mutation has occurred in some member of the population by time t is. The proof of this is somewhat involved so we refer the reader to I wasa et al. K imura considered compensatory mutations that are related to the situation studied in Theorem 4.
Evaluating the expression above numerically, he concluded that the fixation time was surprisingly short. Note that his result covers a different range of parameters since Theorem 4 supposes. However, stochastic tunneling still occurs. Kimura shows that the frequency of single mutants remains small until the second mutation occurs. We use a standard algorithm, described in the next paragraph, to simulate the continuous-time Markov chain X t that counts the number of A mutants in the population at time t.
Readers not interested in the details of our simulation algorithm can skip the next paragraph.