Biology subjects

Macdonald, M.

Publications and source records attributed to Macdonald, M..

3 recordsLinked to original sources

bioRxiv2024

Field evaluation of a volatile pyrethroid spatial repellent and etofenprox-treated clothing for outdoor protection against forest malaria vectors in Cambodia

Cambodias goal to eliminate malaria by 2025 is challenged by persisting transmission in the countrys forest and forest fringe areas. People living in, or traveling to the forest, are exposed to malaria vector bites during the day due to Anopheles daytime biting; and during the night, due to low bed net use and open sleeping structures. Volatile pyrethroid spatial repellents (VPSRs), and insecticide treated clothing (ITC) may help address these gaps in protection. In this field study the authors evaluated the outdoor application of one passive, transfluthrin-based VPSR, four etofenprox-ITCs paired with a picaridin topical repellent, and a combination of VPSR and ITC against wild Anopheles landing in Cambodia. Mathematical modeling was also used to predict the reduction of vectorial capacity of these interventions. A 7x7 Latin-square (6 interventions and one control) was conducted over 49 collection nights in seven temporary, open structures in a forest in Mondulkiri Province, Cambodia. Pairs of participants conducted human landing catches (HLCs) from 18h00 to 06h00, with each collector conducting collections for six hours. A randomly selected subset of collected Anopheles were identified to species using molecular methods. The rate ratio of each intervention compared to the control on Anopheles landings was estimated using a mixed-effect negative binomial regression with intervention, structure, and collector-pair as fixed-effects, and with collection date and structure-night as random effects. The modeling assessment aims to predict the relative reduction in vectoral capacity. Initial calculations involved establishing a "baseline scenario" without intervention, utilizing biometric parameters for Anopheles dirus. Various scenarios accounting for intervention coverage and adherence were then considered. The study aims to update parameters using field study estimates for wild Anopheles, incorporating multiple semi-field estimates for interventions and accounting for the variability and uncertainty in parameter values. Of the total 8,294 Anopheles specimens collected, 15% (n=1,242) of specimens were confirmed to species or species group via PCR. Fifteen species were confirmed; Anopheles dirus Form A was predominant (n=429), followed by Anopheles maculatus (n=189), and Anopheles minimus (n=60). All six interventions reduced Anopheles landing substantially; protective efficacies ranged between 61% (95% confidence interval (CI): 48 - 71%) (etofenprox-ITC, washed) and 95% (95% CI: 93 - 96%) (combined VPSR and unwashed etofenprox-ITC). Finally, the modelling assessment demonstrates significant reductions in vectoral capacity, with the highest impact observed for the combined ITC and VPSR as well as the VPSR used alone, although effectiveness decreases with intervention aging, and variability exists in the magnitude of predicted reductions due to differences in experimental conditions. These transfluthrin-based VPSR and etofenprox ITC interventions have the potential to reduce outdoor and daytime Anopheles biting by providing substantial protection against Anopheles landing. One or more of these tools may play a valuable role in the push for elimination in Cambodia and the Greater Mekong Subregion if programs can achieve effective coverage.

ecology↗

bioRxiv2020

Eigenvector alignment: assessing functional network changes in amnestic mild cognitive impairment and Alzheimer's disease

Eigenvector alignment, introduced herein to investigate human brain functional networks, is adapted from methods developed to detect influential nodes and communities in networked systems. It is used to identify differences in the brain networks of subjects with Alzheimers disease (AD), amnestic Mild Cognitive Impairment (aMCI) and healthy controls (HC). Well-established methods exist for analysing connectivity networks composed of brain regions, including the widespread use of centrality metrics such as eigenvector centrality. However, these metrics provide only limited information on the relationship between regions, with this understanding often sought by comparing the strength of pairwise functional connectivity. Our holistic approach, eigenvector alignment, considers the impact of all functional connectivity changes before assessing the strength of the functional relationship, i.e. alignment, between any two regions. This is achieved by comparing the placement of regions in a Euclidean space defined by the networks dominant eigenvectors. Eigenvector alignment recognises the strength of bilateral connectivity in cortical areas of healthy control subjects, but also reveals degradation of this commissural system in those with AD. Surprisingly little structural change is detected for key regions in the Default Mode Network, despite significant declines in the functional connectivity of these regions. In contrast, regions in the auditory cortex display significant alignment changes that begin in aMCI and are the most prominent structural changes for those with AD. Alignment differences between aMCI and AD subjects are detected, including notable changes to the hippocampal regions. These findings suggest eigenvector alignment can play a complementary role, alongside established network analytic approaches, to capture how the brains functional networks develop and adapt when challenged by disease processes such as AD.

neuroscience↗

bioRxiv2018

Eigenvector-based community detection for identifying information hubs in neuronal networks

Eigenvectors of networked systems are known to reveal central, well-connected, network vertices. Here we expand upon the known applications of eigenvectors to define well-connected communities where each is associated with a prominent vertex. This form of community detection provides an analytical approach for analysing the dynamics of information flow in a network. When applied to the neuronal network of the nematode Caenorhabditis elegans, known circuitry can be identified as separate eigenvector-based communities. For the macaques neuronal network, community detection can expose the hippocampus as an information hub; this result contradicts current thinking that the analysis of static graphs cannot reveal such insights. The application of community detection on a large scale human connectome (~1.8 million vertices) reveals the most prominent information carrying pathways present during a magnetic resonance imaging scan. We demonstrate that these pathways can act as an effective unique identifier for a subjects brain by assessing the number of matching pathways present in any two connectomes.\n\nAuthor summaryThe dynamic response of a network to stimulus can be understood by investigating that systems eigenvectors. The eigenvectors highlight the most prominent nodes; those that are either a major source or destination for information in the network. Moreover by defining a coordinate system based on multiple eigenvectors, the most prominent communities can be detected with the most prominent node detected alongside those in the community that funnel information towards it. These methods are applied to a variety of brain networks to highlight the circuitry present in a flatworm (Caenorhabditis elegans), the macaque and human subjects. Static graphs representing the connectomes are analysed to provide insights that were previously believed to only be detectable by numerically modelling information flow. Finally, we discovered that brain networks created for human subjects at different times can be identified as belonging to the same subject by investigating the similarity of the prominent communities.

neuroscience↗