Biology subjects

Dunham, A.

Publications and source records attributed to Dunham, A..

3 recordsLinked to original sources

bioRxiv2025

Legal but immoral: attitudes toward non-invasive brain stimulation for cognitive enhancement

Cognitive enhancement involves using substances or technologies to improve mental performance in healthy individuals. While methods such as caffeine are widely accepted, others, including prescription stimulants and non-invasive brain stimulation (tDCS), evoke ethical concern. This study examined how university students evaluate three forms of cognitive enhancement (natural, pharmacological, brain stimulation) across five moral domains: academic fairness, free will, naturalness, self-identity, and safety. We also tested how evaluations were shaped by framing (preservation vs. enhancement) and priming (self-affecting vs. non-self-affecting). A total of 449 students completed an online experiment with a 2 x 2 x 3 mixed design, rating enhancement acceptability after reading intervention-specific scenarios. Natural enhancers were judged most positively, followed by brain stimulation and then pharmacological agents. Importantly, although brain stimulation is legal and marketed as non-invasive, it was evaluated more like pharmacological enhancement than natural methods, indicating that legality does not equate to moral acceptability. Framing effects showed that participants were more accepting of enhancement when it was described as preserving ability rather than augmenting it beyond baseline. Priming participants to consider how others use might affect their own outcomes reduced moral acceptability, particularly for pharmacological and brain stimulation methods in the domain of academic fairness. Overall, public attitudes toward enhancement appeared shaped less by legality or objective safety than by intuitive moral reasoning. These findings highlight the importance of adopting pluralistic, context-sensitive approaches to policy and regulation, as student judgments reflect concerns about fairness, authenticity, and competition in academic life.

neuroscience↗

bioRxiv2021

A missense variant effect prediction and annotation resource for SARS-CoV-2

The COVID19 pandemic is a global crisis severely impacting many people across the world. An important part of the response is monitoring viral variants and determining the impact they have on viral properties, such as infectivity, disease severity and interactions with drugs and vaccines. In this work we generate and make available computational variant effect predictions for all possible single amino-acid substitutions to SARS-CoV-2 in order to complement and facilitate experiments and expert analysis. The resulting dataset contains predictions from evolutionary conservation and protein and complex structural models, combined with viral phosphosites, experimental results and variant frequencies. We demonstrate predictions effectiveness by comparing them with expectations from variant frequency and prior experiments. We then identify higher frequency variants with significant predicted effects as well as finding variants measured to impact antibody binding that are least likely to impact other viral functions. A web portal is available at sars.mutfunc.com, where the dataset can be searched and downloaded.

bioinformatics↗

bioRxiv2020

Exploring amino acid functions in a deep mutational landscape

Amino acids fulfil a diverse range of roles in proteins, each utilising its chemical properties in different ways in different contexts to create required functions. For example, cysteines form disulphide or hydrogen bonds in different circumstances and charged amino acids do not always make use of their charge. The repertoire of amino acid functions and the frequency at which they occur in proteins remains understudied. Measuring large numbers of mutational consequences, which can elucidate the role an amino acid plays, was prohibitively time consuming until recent developments in deep mutational scanning. In this study we gathered data from 28 deep mutational scanning studies, covering 6291 positions in 30 proteins, and used the consequences of mutation at each position to define a mutational landscape. We demonstrated rich relationships between this landscape and biophysical or evolutionary properties. Finally, we identified 100 functional amino acid subtypes with a data-driven clustering analysis and studied their features, including their frequencies and chemical properties such as tolerating polarity, hydrophobicity or being intolerant of charge or specific amino acids. The mutational landscape and amino acid subtypes provide a foundational catalogue of amino acid functional diversity, which will be refined as the number of studied protein positions increases.

systems biology↗