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1. A spatial mutation model with increasing mutation rates

   https://doi.org/10.1017/jpr.2022.120  

   Chao, Brian; Schweinsberg, Jason (December 2023, Journal of Applied Probability)

   Abstract We consider a spatial model of cancer in which cells are points on thed-dimensional torus$$\mathcal{T}=[0,L]^d$$, and each cell with$$k-1$$mutations acquires akth mutation at rate$$\mu_k$$. We assume that the mutation rates$$\mu_k$$are increasing, and we find the asymptotic waiting time for the first cell to acquirekmutations as the torus volume tends to infinity. This paper generalizes results on waiting for$$k\geq 3$$mutations in Fooet al.(2020), which considered the case in which all of the mutation rates$$\mu_k$$are the same. In addition, we find the limiting distribution of the spatial distances between mutations for certain values of the mutation rates. 

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2. Mutation Coverage is not Strongly Correlated with Mutation Coverage

   https://doi.org/10.1145/3644032.3644442  

   Alblwi, Samia; Ayad, Amani; Mili, Ali (April 2024, ACM)

   Several metrics have been proposed in the past to quantify the effectiveness of a test suite; they are usually based on some measure of coverage because it is sensible to quantify the effectiveness of a test suite by the extent to which it exercises (covers) various syntactic features of the program under test. Though no coverage metric has emerged as the gold standard of test suite effectiveness, mutation coverage is widely perceived as a reliable measure of test suite effectiveness because the ability of a test suite to detect program mutations can be used as an indication of its ability to detect actual faults. In this paper we aim to challenge the superiority of mutation coverage, by showing that the same test suite may have vastly different values of mutation coverage depending on the mutation operators that are used in the estimation. 

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3. Mutation Analysis for Coq

   https://doi.org/10.1109/ASE.2019.00057  

   Celik, Ahmet; Palmskog, Karl; Parovic, Marinela; Jesus Gallego Arias, Emilio; Gligoric, Milos (November 2019, International Conference on Automated Software Engineering)
4. Contextual Predictive Mutation Testing

   https://doi.org/10.1145/3611643.3616289  

   Jain, Kush; Alon, Uri; Groce, Alex; Le_Goues, Claire (November 2023, ACM)
5. Similar mutation rates but different mutation spectra in moderate and extremely halophilic archaea

   https://doi.org/10.1093/g3journal/jkac303  

   Kucukyildirim, Sibel; Ozdemirel, Huseyin Ozgur; Lynch, Michael (December 2022, G3 Genes|Genomes|Genetics)

   Wong, A (Ed.)

   Abstract Archaea are a major part of Earth’s microbiota and extremely diverse. Yet, we know very little about the process of mutation that drives such diversification. To expand beyond previous work with the moderate halophilic archaeal species Haloferax volcanii, we performed a mutation-accumulation experiment followed by whole-genome sequencing in the extremely halophilic archaeon Halobacterium salinarum. Although Hfx. volcanii and Hbt. salinarum have different salt requirements, both species have highly polyploid genomes and similar GC content. We accumulated mutations for an average of 1250 generations in 67 mutation accumulation lines of Hbt. salinarum, and revealed 84 single-base substitutions and 10 insertion-deletion mutations. The estimated base-substitution mutation rate of 3.99 × 10−10 per site per generation or 1.0 × 10−3 per genome per generation in Hbt. salinarum is similar to that reported for Hfx. volcanii (1.2 × 10−3 per genome per generation), but the genome-wide insertion-deletion rate and spectrum of mutations are somewhat dissimilar in these archaeal species. The spectra of spontaneous mutations were AT biased in both archaea, but they differed in significant ways that may be related to differences in the fidelity of DNA replication/repair mechanisms or a simple result of the different salt concentrations. 

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