PMC

Branch errors for simulated E. coli datasets. Simulated E. coli trees are shown for medium mutation rate (0.0001 per base per branch). (A) shows branch length errors as bars, with overestimates of branch length above each branch and underestimates below each branch. Maximum overestimate of branch length was 2.15% (bars above each branch) and maximum underestimate was 4.73% (bars below each branch). (B) shows branch SNP errors as bars, with false-positive errors above each branch and false-negative errors below each branch. The maximum FP SNP value is 6 (bars above each branch) and maximum FN SNP value is 23 (bars below each branch). Note that the bar heights have been normalized by the maximum value for each tree and are not comparable across trees. Outlier results from Mugsy were excluded from the branch length plot, and kSNP results are not shown. All genome alignment methods performed similarly on closed genomes, with Mauve and Mugsy exhibiting the best sensitivity (Table ).

Branch points with greater history dependence are less modifiable. A, Percentage reduction in the probability of targeted transitions (“percentage learning”) for 13 branch points (triangle and bars indicate mean ± SD; n = 13 experiments in which the dominant transition was targeted). B, The age of birds at onset of WN playback was not significantly correlated with percentage learning (r = 0.22; p = 0.46). C, The initial transition probability (measured at baseline) was not significantly correlated with percentage learning (r = 0.11; p = 0.71). D–F, Three branch points illustrating varying degrees of history dependence (differences in transition probabilities at a branch point depending on which transition had occurred at the prior instance of the branch point). D shows conditional probabilities for the branch point in our study that exhibited the highest level of history dependence (branch point 1). The probability of the “ab” transition when an “ab” transition had occurred at the prior branch point, p(ab_n|ab_n−1), was 0.04. However, the probability of the “ab” transition when an “ac” transition had occurred at the prior branch point, p(ab_n|ac_n−1), was 0.96. To quantify history dependence for branch points, we computed the absolute value of the difference between these conditional transition probabilities, |p(ab_n|ac_n−1 − p(ab_n|ab_n−1)| = 0.92. E shows conditional probabilities for a branch point that exhibited less, but still significant, history dependence (branch point 2). The absolute value of the difference between conditional transition probabilities was 0.4. F shows conditional transition probabilities for a branch point that did not exhibit significant history dependence (branch point 3). The absolute value of the difference between conditional transition probabilities was 0.04. G, History dependence for 13 branch points. Data are ordered vertically according to increasing history dependence. The red triangles denote branch points for which the history dependence was significantly different from 0 (“history-dependent”; n = 6 branch points, where values fall outside 95% confidence intervals determined by resampling). Points labeled “(1),” “(2),” and “(3)” correspond to the three branch points shown in D–F. H, The history dependence of branch points was significantly correlated with observed variation in percentage learning (r = −0.71; p = 0.007). The red symbols correspond to branch points with significant history dependence. The triangles indicate means for branch points that exhibited significant history dependence (red) and for branch points that did not exhibit significant history dependence (black).

Reduced datasets were used to investigate the impact of model-choice on branch lengths. A. A phylogenetic tree for 21 fish taxa. B. A plot showing differences between branch lengths estimated under partition-specific matrices and the mtFishR0 matrix. C. A plot showing the differences between the corrected branch lengths under a mixture of the partition specific matrices and mtFishR0 for the whole data. Differences between branch lengths (Bl) are measured as (Bl_R_i/Bl_R₀) – 1, where Bl_R_i denotes branch lengths obtained using a partition specific matrix, and Bl_R₀ denotes branch lengths obtained using the reference matrix (mtFishR0). This measure centers the difference between branch lengths on 0, with values above 0 indicating branches that were larger under the partition-specific matrix and values below 0 indicating branches that were shorter under the partition-specific matrix. A value of zero indicates no difference between branch lengths.

The improved invasive blood pressure monitoring pipeline valve. The improved invasive blood pressure monitoring pipeline valve includes four interconnected pipeline branches (1, 2, 3, 4) and a valve switch (5) that controls the opening and closing of the pipeline branches; the first pipeline branch () is connected to the arterial catheter (10), the second pipeline branch (2) is equipped with a pressure sensor screw cap (20), the third pipeline branch (3) is connected with the pressure sensor pipeline (30); the fourth pipeline branch (4) is equipped with a positive-pressure connector (40). When in use, the monitor connected to the pressure sensor needs to be reset to zero, whereas the second pipeline branch (2) is normally closed (A); for zeroing operation, the valve switch is first turned to the first pipeline branch (B); at this time, the first pipeline branch is closed, and the pressure sensor path of the monitor is connected to the air and atmospheric pressure. When the monitor reset button is pressed, and the monitor value shows 0, the valve switch turns to the second pipeline branch (A). At this time, the first pipeline branch, the third pipeline branch, and the fourth pipeline branch are connected, and the second pipeline branch is closed; when blood is collected, the syringe nipple is directly connected to the positive-pressure connector to draw blood.