Biology subjects

Taylor, D. A.

Publications and source records attributed to Taylor, D. A..

4 recordsLinked to original sources

bioRxiv2026

An adversarial approach to guide the selection of preprocessing pipelines for ERP studies

Electroencephalography (EEG) data are inherently contaminated by non-neuronal noise, including eye movements, muscle activity, cardiac signals, electrical interference, and technical issues such as poorly connected electrodes. Preprocessing to remove these artefacts is essential, yet the optimal method remains unclear due to the vast number of available techniques, their combinatorial use in pipelines, and adjustable parameters. Consequently, most studies adopt ad hoc preprocessing strategies based on dataset characteristics, study goals, and researcher expertise, with little justification for their choices. Such variability can influence downstream results, potentially determining whether effects are detected, and introduces risks of questionable analytical practices. Here, we present a method to objectively evaluate and compare preprocessing pipelines. Our approach uses realistically simulated signals injected into real EEG data as "ground truth", enabling the assessment of a pipelines ability to remove noise without distorting neuronal signals. This evaluation is independent of the studys main analyses, ensuring that pipeline selection does not bias results. By applying this procedure, researchers can select preprocessing strategies that maximize signal-to-noise ratio while maintaining the integrity of the neural signal, improving both reproducibility and interpretability of EEG studies. Although the data presented here focuses on processing and analysis most relevant for ERP research, the method can be flexibly expanded to other types of analyses or signals.

neuroscience↗

bioRxiv2021

Testing methods for quantifying Monte Carlo variation for categorical variables in Probabilistic Genotyping

Two methods for applying a lower bound to the variation induced by the Monte Carlo effect are trialled. One of these is implemented in the widely used probabilistic genotyping system, STRmix. Neither approach is giving the desired 99% coverage. In some cases the coverage is much lower than the desired 99%. The discrepancy (i.e. the distance between the LR corresponding to the desired coverage and the LR observed coverage at 99%) is not large. For example, the discrepancy of 0.23 for approach 1 suggests the lower bounds should be moved downwards by a factor of 1.7 to achieve the desired 99% coverage. Although less effective than desired these methods provide a layer of conservatism that is additional to the other layers. These other layers are from factors such as the conservatism within the sub-population model, the choice of conservative measures of co-ancestry, the consideration of relatives within the population and the resampling method used for allele probabilities, all of which tend to understate the strength of the findings. HighlightsO_LITwo methods for quantifying Monte Carlo variability are tested, C_LIO_LIBoth give less than the desired 99% coverage, C_LIO_LIThe magnitude of possible discrepancy is small, C_LIO_LIFor example an LR of 4.3 x 1011 could be reported as 1.8 x 1012 C_LIO_LIAn LR of 18 could be reported as 22. C_LI

molecular biology↗

bioRxiv2021

Revisiting the STRmixlikelihood ratio probability interval coverage considering multiple factors

In previously reported work a method for applying a lower bound to the variation induced by the Monte Carlo effect was trialled. This is implemented in the widely used probabilistic genotyping system, STRmix. The approach did not give the desired 99% coverage. However, the method for assigning the lower bound to the MCMC variability is only one of a number of layers of conservativism applied in a typical application. We tested all but one of these sources of variability collectively and term the result the near global coverage. The near global coverage for all tested samples was greater than 99.5% for inclusionary average LRs of known donors. This suggests that when included in the probability interval method the other layers of conservativism are more than adequate to compensate for the intermittent underperformance of the MCMC variability component. Running for extended MCMC accepts was also shown to result in improved precision.

molecular biology↗

bioRxiv2019

Impaired Therapeutic Efficacy of Bone Marrow Cells from Post-Myocardial Infarction Patients in the TIME and LateTIME Clinical Trials

Implantation of bone marrow-derived cells (BMCs) into mouse hearts post-myocardial infarction (MI) limits cardiac functional decline. However, clinical trials of post-MI BMC therapy have yielded conflicting results. While most laboratory experiments use healthy BMC donor mice, clinical trials use post-MI autologous BMCs. Post-MI mouse BMCs are therapeutically impaired, due to inflammatory changes in BMC composition. Thus, therapeutic efficacy of the BMCs progressively worsens after MI but recovers as donor inflammatory response resolves. The availability of post-MI patient BM mononuclear cells (MNCs) from the TIME and LateTIME clinical trials enabled us to test if human post-MI MNCs undergo a similar period of impaired efficacy. We hypothesized that MNCs from TIME trial patients would be less therapeutic than healthy human donor MNCs when implanted into post-MI mouse hearts, and that therapeutic properties would be restored in MNCs from LateTIME trial patients. Post-MI SCID mice received MNCs from healthy donors, TIME patients, or LateTIME patients. Cardiac function improved considerably in the healthy donor group, but neither the TIME nor LateTIME group showed therapeutic effect. Conclusion: post-MI human MNCs lack therapeutic benefits possessed by healthy MNCs, which may partially explain why BMC clinical trials have been less successful than mouse studies.

physiology↗