Accuracy of ancestral state reconstruction for non-neutral traits.

The assumptions underpinning ancestral state reconstruction are violated in many evolutionary systems, especially for traits under directional selection. However, the accuracy of ancestral state reconstruction for non-neutral traits is poorly understood. To investigate the accuracy of ancestral state reconstruction methods, trees and binary characters were simulated under the BiSSE (Binary State Speciation and Extinction) model using a wide range of character-state-dependent rates of speciation, extinction and character-state transition. We used maximum parsimony (MP), BiSSE and two-state Markov (Mk2) models to reconstruct ancestral states. Under each method, error rates increased with node depth, true number of state transitions, and rates of state transition and extinction; exceeding 30% for the deepest 10% of nodes and highest rates of extinction and character-state transition. Where rates of character-state transition were asymmetrical, error rates were greater when the rate away from the ancestral state was largest. Preferential extinction of species with the ancestral character state also led to higher error rates. BiSSE outperformed Mk2 in all scenarios where either speciation or extinction was state dependent and outperformed MP under most conditions. MP outperformed Mk2 in most scenarios except when the rates of character-state transition and/or extinction were highly asymmetrical and the ancestral state was unfavoured.

Accurate and Unbiased Quantitation of Amyloid-ß Fluorescence Images Using ImageSURF.

BACKGROUND: Images of amyloid-ß pathology characteristic of Alzheimer's disease are difficult to consistently and accurately segment, due to diffuse deposit boundaries and imaging variations. METHODS: We evaluated the performance of ImageSURF, our open-source ImageJ plugin, which considers a range of image derivatives to train image classifiers. We compared ImageSURF to standard image thresholding to assess its reproducibility, accuracy and generalizability when used on fluorescence images of amyloid pathology. RESULTS: ImageSURF segments amyloid-ß images significantly more faithfully, and with significantly greater generalizability, than optimized thresholding. CONCLUSION: In addition to its superior performance in capturing human evaluations of pathology images, ImageSURF is able to segment image sets of any size in a consistent and unbiased manner, without requiring additional blinding, and can be retrospectively applied to existing images. The training process yields a classifier file which can be shared as supplemental data, allowing fully open methods and data, and enabling more direct comparisons between different studies.