scholarly journals Joint Alignment From Pairwise Differences with a Noisy Oracle

2020 ◽  
Author(s):  
Charalampos Tsourakakis ◽  
Michael Mitzenmacher
Keyword(s):  
Pairwise Differences

scholarly journals Pairwise comparisons of mitochondrial DNA sequences in stable and exponentially growing populations.

Genetics ◽  
1991 ◽  
Vol 129 (2) ◽  
pp. 555-562 ◽  
Author(s):  
M Slatkin ◽  
R R Hudson
Keyword(s):  
Growth Rate ◽  
Mitochondrial Dna ◽  
Population Size ◽  
Dna Sequences ◽  
Geometric Distribution ◽  
Goodness Of Fit Test ◽  
Sample Distribution ◽  
Constant Size ◽  
Current Population ◽  
Pairwise Differences

Abstract We consider the distribution of pairwise sequence differences of mitochondrial DNA or of other nonrecombining portions of the genome in a population that has been of constant size and in a population that has been growing in size exponentially for a long time. We show that, in a population of constant size, the sample distribution of pairwise differences will typically deviate substantially from the geometric distribution expected, because the history of coalescent events in a single sample of genes imposes a substantial correlation on pairwise differences. Consequently, a goodness-of-fit test of observed pairwise differences to the geometric distribution, which assumes that each pairwise comparison is independent, is not a valid test of the hypothesis that the genes were sampled from a panmictic population of constant size. In an exponentially growing population in which the product of the current population size and the growth rate is substantially larger than one, our analytical and simulation results show that most coalescent events occur relatively early and in a restricted range of times. Hence, the "gene tree" will be nearly a "star phylogeny" and the distribution of pairwise differences will be nearly a Poisson distribution. In that case, it is possible to estimate r, the population growth rate, if the mutation rate, mu, and current population size, N0, are assumed known. The estimate of r is the solution to ri/mu = ln(N0r) - gamma, where i is the average pairwise difference and gamma approximately 0.577 is Euler's constant.

scholarly journals Time in Specific Glucose Ranges, Glucose Management Indicator, and Glycemic Variability: Impact of Continuous Glucose Monitoring (CGM) System Model and Sensor on CGM Metrics

2020 ◽  
pp. 193229682093182
Author(s):  
Stefan Pleus ◽  
Ulrike Kamecke ◽  
Delia Waldenmaier ◽  
Manuela Link ◽  
Eva Zschornack ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
International Consensus ◽  
Freestyle Libre ◽  
Pairwise Differences ◽  
Assess Quality ◽  
Glucose Management ◽  
Post Hoc ◽  
The Impact

Background: International consensus recommends a set of continuous glucose monitoring (CGM) metrics to assess quality of diabetes therapy. The impact of individual CGM sensors on these metrics has not been thoroughly studied yet. This post hoc analysis aimed at comparing time in specific glucose ranges, coefficient of variation (CV) of glucose concentrations, and glucose management indicator (GMI) between different CGM systems and different sensors of the same system. Method: A total of 20 subjects each wore two Dexcom G5 (G5) sensors and two FreeStyle Libre (FL) sensors for 14 days in parallel. Times in ranges, GMI, and CV were calculated for each 14-day sensor experiment, with up to four sensor experiments per subject. Pairwise differences between different sensors of the same CGM system as well as between sensors of different CGM system were calculated for these metrics. Results: Pairwise differences between sensors of the same model showed larger differences and larger variability for FL than for G5, with some subjects showing considerable differences between the two sensors. When pairwise differences between sensors of different CGM models were calculated, substantial differences were found in some subjects (75th percentiles of differences of time spent <70 mg/dL: 5.0%, time spent >180 mg/dL: 9.2%, and GMI: 0.42%). Conclusion: Relevant differences in CGM metrics between different models of CGM systems, and between different sensors of the same model, worn by the same study subjects were found. Such differences should be taken into consideration when these metrics are used in the treatment of diabetes.

scholarly journals Tests for Scale Changes Based on Pairwise Differences

2019 ◽  
Vol 115 (531) ◽  
pp. 1336-1348
Author(s):  
Carina Gerstenberger ◽  
Daniel Vogel ◽  
Martin Wendler
Keyword(s):  
Pairwise Differences

scholarly journals Using the variance of pairwise differences to estimate the recombination rate

1997 ◽  
Vol 69 (1) ◽  
pp. 45-48 ◽  
Author(s):  
JOHN WAKELEY
Keyword(s):  
Population Size ◽  
Standard Error ◽  
Recombination Rate ◽  
Population Model ◽  
Finite Population ◽  
Pairwise Differences ◽  
Theoretical Developments

A new estimator is proposed for the parameter C=4Nc, where N is the population size and c is the recombination rate in a finite population model without selection. The estimator is an improved version of Hudson's (1987) estimator, which takes advantage of some recent theoretical developments. The improvement is slight, but the smaller bias and standard error of the new estimator support its use. The variance of the average number of pairwise differences is also derived, and is important in the formulation of the new estimator.

Sign in / Sign up

Export Citation Format

Share Document