Biology subjects

Michael, E.

Publications and source records attributed to Michael, E..

6 recordsLinked to original sources

bioRxiv2023

The causal roles of parietal alpha oscillations and evoked potentials in coding task-relevant information during selective attention

Selective attention is a fundamental cognitive mechanism that allows people to prioritise task-relevant information while ignoring irrelevant information. Previous research has suggested key roles of parietal evoked potentials and alpha oscillatory responses in spatial attention tasks. However, the informational content of these signals is less clear, and their causal effects on the coding of multiple task elements are yet unresolved. Here, we used concurrent TMS-EEG to causally manipulate parietal alpha power and evoked potentials and investigate their roles in coding multiple task features (where to attend, what to attend to, and visual stimulus) in a selective attention task. First, using EEG-only data, we found that evoked potentials coded all three types of task-relevant information with distinct temporal dynamics, and alpha oscillations carried information regarding both where to attend and what to attend to. Then, we applied rhythmic-TMS (rTMS) at individual alpha frequency over the right intraparietal sulcus (IPS), while concurrently measuring EEG. Compared with control arrhythmic-TMS, alpha rTMS increased alpha power and inter-trial phase coherence and yielded more negative posterior-contralateral evoked potentials. Moreover, alpha rTMS causally and specifically improved multivariate decoding of the information about where to attend (but not what to attend to or feature information) during task performance, with decoding improvements predicting changes in behavioural performance. These findings illuminate the dynamics with which the complementary aspects of a selective attention task are encoded in evoked and oscillatory brain activity. Moreover, they reveal a specific and causal role of IPS-controlled evoked and oscillatory activity in carrying behaviour-driving information about where to focus attention.

neuroscience↗

bioRxiv2020

Proteus software for physics-based protein design

We describe methods and software for physics-based protein design. The folded state energy combines molecular mechanics with Generalized Born solvent. Sequence and conformation space are sampled with Replica Exchange Monte Carlo, assuming one or a few fixed protein backbone structures and discrete side chain rotamers. Whole protein design and enzyme design are presented as illustrations. Full redesign of three PDZ domains was done using a simple, empirical, unfolded state model. Designed sequences were very similar to natural ones. Enzyme redesign exploited a powerful, adaptive, importance sampling approach that allows the design to directly target substrate binding, reaction rate, catalytic efficiency, or the specificity of these properties. Redesign of tyrosyl-tRNA synthetase stereospecificity is reported as an example.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biophysics↗

bioRxiv2019

Predicting lymphatic filariasis elimination in data-limited settings: a reconstructive computational framework for combining data generation and model discovery

Although there is increasing recognition of the importance of mathematical models in the effective design and management of long-term parasite elimination, it is also becoming clear that to be most useful parasite transmission models must accurately reflect the processes pertaining to local infection dynamics. These models must also be identified even when the data required for characterizing the local transmission process are limited or incomplete, as is often the case for neglected tropical diseases, including the disease system studied in this work, viz. lymphatic filariasis (LF). Here, we draw on progress made in the field of computational knowledge discovery to present a reconstructive simulation framework that addresses these challenges by facilitating the discovery of data and models concurrently in order to deliver reliable location-specific predictions pertaining to LF elimination in areas where we have insufficient observational data. Using available data from eight sites from Nigeria and elsewhere, we demonstrate that our data-model discovery system is able to identify local transmission models using a generalized knowledge of filarial transmission dynamics, monitoring survey data, and details of historical interventions, which in turn can also generate information regarding the pre-control endemic status of LF in each study site. Forecasts of the impacts of interventions made by the models discovered using the reconstructed baseline data not only matched temporal infection observations, but also provided critical information regarding when transmission interruption is likely to have occurred. Assessments of elimination and recrudescence probabilities based on these models also suggested a protective effect of vector control against the reemergence of transmission after stopping drug treatments. The reconstructive computational framework for model and data discovery developed here highlights how coupling models with available data can generate new knowledge about complex, data-limited systems, and ultimately support the effective management of disease programs in the face of critical data gaps.\n\nAuthor summaryAs modelling becomes commonly used in the design and evaluation of parasite elimination programs, the need for well-defined models and datasets describing the nature of transmission processes in endemic settings is becoming pronounced. For many neglected tropical diseases, including the disease investigated in this study, lymphatic filariasis (LF), however, data for model identification are typically sparse or incomplete. In this study, we present a new data-model computational discovery system that couples data-assimilation methods based on existing monitoring survey data with model-generated data about baseline conditions, in order to discover the local LF transmission models required for simulating the impacts of interventions for achieving parasite elimination in typical endemic locations. Using data from eight study sites in Nigeria and elsewhere, we show that our reconstructive computational knowledge discovery system is able to combine information contained within partially-available site-specific monitoring data with knowledge of parasite transmission dynamics embedded in process-based models to generate the missing data required for inducing reliable locally applicable LF models. We also show that the models so discovered are able to generate the intervention forecasts required for supporting management-relevant decisions in parasite elimination.

ecology↗

bioRxiv2019

Adaptation to binocular anticorrelation results in increased neural excitability

Throughout the brain, information from individual sources converges onto higher order neurons. For example, information from the two eyes first converges in binocular neurons in area V1. Many neurons appear tuned to similarities between sources of information, which makes intuitive sense in a system striving to match multiple sensory signals to a single external cause, i.e., establish causal inference. However, there are also neurons that are tuned to dissimilar information. In particular, many binocular neurons respond maximally to a dark feature in one eye and a light feature in the other. Despite compelling neurophysiological and behavioural evidence supporting the existence of these neurons (Cumming & Parker, 1997; Janssen, Vogels, Liu, & Orban, 2003; Katyal, Vergeer, He, He, & Engel, 2018; Kingdom, Jennings, & Georgeson, 2018; Tsao, Conway, & Livingstone, 2003), their function has remained opaque. To determine how neural mechanisms tuned to dissimilarities support perception, here we use electroencephalography to measure human observers steady-state visually evoked potentials (SSVEPs) in response to change in depth after prolonged viewing of anticorrelated and correlated random-dot stereograms (RDS). We find that adaptation to anticorrelated RDS results in larger SSVEPs, while adaptation to correlated RDS has no effect. These results are consistent with recent theoretical work suggesting what not neurons play a suppressive role in supporting stereopsis (Goncalves & Welchman, 2017); that is, selective adaptation of neurons tuned to binocular mismatches reduces suppression resulting in increased neural excitability.

neuroscience↗

bioRxiv2018

Economic performance and cost-effectiveness of using a DEC-salt social enterprise for eliminating the major neglected tropical disease, lymphatic filariasis

BackgroundSalt fortified with the drug, diethylcarbamazine (DEC), and introduced into a competitive market has the potential to overcome the obstacles associated with tablet-based Lymphatic Filariasis (LF) elimination programs. Questions remain, however, regarding the economic viability, production capacity, and effectiveness of this strategy as a sustainable means to bring about LF elimination in resource poor settings.\n\nMethodology and Principal FindingsWe evaluated the performance and effectiveness of a novel social enterprise-based approach developed and tested in Leogane, Haiti, as a strategy to sustainably and cost-efficiently distribute DEC-medicated salt into a competitive market at quantities sufficient to bring about the elimination of LF. We undertook a cost-revenue analysis to evaluate the production capability and financial feasibility of the developed DEC salt social enterprise, and a modeling study centered on applying a dynamic mathematical model localized to reflect local LF transmission dynamics to evaluate the cost-effectiveness of using this intervention versus standard annual Mass Drug Administration (MDA) for eliminating LF in Leogane. We show that the salt enterprise because of its mixed product business strategy may have already reached the production capacity for delivering sufficient quantities of edible DEC-medicated salt to bring about LF transmission in the Leogane study setting. Due to increasing revenues obtained from the sale of DEC salt over time, expansion of its delivery in the population, and greater cumulative impact on the survival of worms, this strategy could also represent a significantly more cost-effective option than annual DEC tablet-based MDA for accomplishing LF elimination.\n\nSignificanceA social enterprise approach can offer an innovative market-based strategy by which edible salt fortified with DEC could be distributed to communities both on a financially sustainable basis and at sufficient quantity to eliminate LF. Deployment of similarly fashioned intervention strategies would improve current efforts to successfully accomplish the goal of LF elimination, particularly in difficult-to-control settings.\n\nAuthor summaryWith less than three years remaining for meeting the 2020 target set by WHO for accomplishing the global elimination of Lymphatic Filariasis (LF), concerns are emerging regarding the feasibility of meeting this goal using the current tablet-based Mass Drug Administration strategy. Salt fortified with the antifilarial drug, diethylcarbamazine (DEC), could offer an intervention that avoids many of the barriers connected with tablet-based elimination programs. We analyzed the economic performance and cost-effectiveness of a novel DEC-salt social enterprise developed and tested in Leogane arrondissement, Haiti, as a particularly significant strategy for accomplishing sustainable LF elimination in such complex settings. We show that because of increasing revenue from the sale of the DEC salt over time, expansion of its delivery in the population, and the adverse effect of continuous consumption of the drug on worms, the delivery of DEC through a salt enterprise can represent a significantly more cost-effective option than annual DEC tablet-based MDA for accomplishing LF elimination in settings, like Leogane. We indicate that development of policy and research into how to deploy similarly-fashioned interventions, or work with the salt industry to increase population use of medicated salt, would improve present efforts to successfully accomplish the elimination of LF.

epidemiology↗

bioRxiv2017

Adaptive gain control in the human dorsal anterior cingulate cortex

When making decisions, humans are often distracted by irrelevant information. Distraction has different impact on perceptual, cognitive and value-guided choices, giving rise to well-described behavioural phenomena such as the tilt illusion, conflict adaptation, or economic decoy effects. However, a single, unified model that can account for all these phenomena has yet to emerge. Here, we offer one such account, based on adaptive gain control, and additionally show that it successfully predicts a range of counterintuitive new behavioural phenomena on variants of a classic cognitive paradigm, the Eriksen flanker task. We also report that BOLD signals in a dorsal network prominently including the anterior cingulate cortex (dACC), index a gain-modulated decision variable predicted by the model. This work unifies the study of distraction across perceptual, cognitive and economic domains.

neuroscience↗