Biology subjects

Alexander, N.

Publications and source records attributed to Alexander, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Secure Attachment despite Childhood Maltreatment: Behavioral and Neural Correlates of Interpersonal Resilience

BackgroundChildhood maltreatment is a traumatic interpersonal stressor that increases vulnerability for depression in adulthood. However, some individuals show secure attachment despite childhood maltreatment, a pattern that can be described as interpersonal resilience. The present study examined the behavioral and neural correlates of interpersonal resilience, defined as secure attachment in adults exposed to childhood maltreatment. MethodsWe analyzed structural 3T MRI data from 1,317 adults, including healthy participants and individuals with partially or fully remitted major depression. Gray matter volume was estimated from structural MRI data using voxel-based morphometry. Attachment style and childhood maltreatment were assessed using the Relationship Scales Questionnaire and the Childhood Trauma Questionnaire, respectively. A 2x2 design (childhood maltreatment by attachment style) tested main and interaction effects on behavioral outcomes and brain structure. ResultsInterpersonally resilient individuals with secure attachment and maltreatment reported significantly better mental health outcomes compared to insecurely attached adults with maltreatment. Differences included lower self-reported and rater-based depressive symptoms, lower global symptom severity, and higher global functioning. In the neuroimaging analyses, we identified a significant childhood maltreatment by attachment style interaction in the left supramarginal gyrus, with larger gray matter volume in resilient individuals compared to all other groups. This effect remained robust across multiple sensitivity analyses, controlling for medication load, antidepressant intake, diagnosis group, as well as in a complementary dimensional analysis. ConclusionsThe results identify a potential neural correlate of interpersonal resilience. Larger gray matter volume in the left supramarginal gyrus, a region previously implicated in perspective taking and self-other distinction among other functions, may be relevant to more adaptive interpersonal functioning after early adversity. Together with the robust behavioral effects, these findings are consistent with secure attachment as a protective factor that may be associated with attenuated effects of childhood maltreatment on mental health.

neuroscience↗

bioRxiv2026

The genetics of transcriptional responses to stress in yeast

Alteration of gene expression levels is a key cellular strategy for responding to internal and external challenges. While expression quantitative trait loci (eQTLs) are known to influence steady-state transcript levels, their role in mediating rapid physiological transitions remains poorly understood. Using a high-throughput, "one-pot" single-cell RNA-seq approach, we mapped eQTLs in yeast immediately before and after two acute environmental shifts: salt perturbation of actively cycling cells and nutrient repletion of cells after one week of starvation. We defined and characterized major cell populations in different physiological states and identified context-specific eQTLs as well as genetic determinants of state occupancy. We focused on distant eQTL hotspots, which capture the majority of expression heritability in yeast crosses. We found that the effects of hotspots on gene expression are highly dynamic in response to acute environmental perturbations, and that they influence biological processes directly relevant to adaptation to the new conditions. By integrating gene set enrichment analysis with computational prediction of variant effects, we prioritized candidate causal genes and variants underlying the hotspots. We observed extensive overlaps between hotspot loci, physiological state QTL, and fitness QTL. These findings reveal how regulatory variation rapidly modulates cellular responses to environmental changes, suggesting a mechanistic bridge between genetic variation, gene expression, and organismal fitness.

genetics↗

bioRxiv2026

Interpretable Hierarchical RNNs for rs-fMRI: Promise and Limits of Individualized Brain Dynamics

Modeling individual brain dynamics from resting-state fMRI (rs-fMRI) remains challenging due to substantial inter-subject variability, noise, and limited data length per subject. Here, we systematically evaluate whether hierarchical shallow piecewise-linear recurrent neural networks (shPLRNNs), recently introduced as interpretable dynamical system reconstruction models, can generate individualized rs-fMRI time series while preserving subject-specific functional connectivity structure. We applied the framework to 1,423 rs-fMRI samples from healthy participants of the Marburg-Munster Affective Disorders Cohort Study (MACS). Simulated rs-fMRI data reproduced substantial empirical FC structure, with comparable reconstruction accuracy on the validation and held-out test sets. Generalization to unseen individuals was heterogeneous and strongly depended on how typical a subjects connectivity pattern was relative to the training cohort, with template similarity explaining 37% of variance in reconstruction accuracy. Learned subject-specific parameters exhibited significant test-retest stability and higher within-subject than between-subject similarity on longitudinal data from two different timepoints, supporting their interpretation as individualized dynamical markers. Associations between individual parameters and demographic or cognitive variables were statistically significant but modest in effect size, and predictive performance remained below that obtained using empirical rs-fMRI features directly. Empirical FC was used as a reference for static subject information rather than as a target to be outperformed. Together, these results suggest that hierarchical shPLRNNs can extract meaningful and partially stable individual-specific dynamical structure from rs-fMRI data. The findings delineate key trade-offs between model expressivity, generalization and subject specificity, and point to directions for future methodological refinement in individualized brain modeling. Graphical AbstractA hierarchical dynamical RNN captures substantial individual rs-fMRI functional connectivity structure using compact subject-specific parameters embedded in shared population dynamics. The resulting representations generalize to held-out subjects and show test-retest stability, but only modest associations with phenotypic variables. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/713153v3_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@1b029edorg.highwire.dtl.DTLVardef@90bdd6org.highwire.dtl.DTLVardef@9f6b77org.highwire.dtl.DTLVardef@486c9e_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Wearable MEG data recorded during human stepping

Non-invasive spatiotemporal imaging of brain activity during large-scale, whole body movement is a significant methodological challenge for the field of movement neuroscience. Here, we present a dataset recorded using a new imaging modality - optically-pumped magnetoencephalography (OP-MEG) - to record brain activity during human stepping. Participants (n=3) performed a visually guided stepping task requiring precise foot placement while dual-axis and triaxial OP-MEG and leg muscle activity (electromyography, EMG) were recorded. The dataset also includes a structural MRI for each participant and foot kinematics. We validate the fidelity of the OPM data by showing movement-related beta band desynchronization source localised to the primary motor cortex. This multimodal dataset offers a resource for methodological development and testing for OPM data (e.g., movement-related interference rejection), within-subject analyses, and exploratory analyses to generate hypotheses for further work on the neural control of human stepping.

neuroscience↗

bioRxiv2024

Gray matter correlates of childhood maltreatment in the context of major depression: searching for replicability in a multi-cohort brain-wide association study of 3225 adults

Childhood maltreatment effects on cerebral gray matter have been frequently discussed as a neurobiological pathway for depression. However, localizations are highly heterogeneous, and recent reports have questioned the replicability of mental health neuroimaging findings. Here, we investigate the replicability of gray matter correlates of maltreatment (measured retrospectively via the Childhood Trauma Questionnaire) across three large adult cohorts (total N=3225). Pooling cohorts revealed maltreatment-related gray matter reductions, with most extensive effects when not controlling for depression diagnosis (maximum partial R2=.022). However, none of these effects significantly replicated across cohorts. Non-replicability was consistent across a variety of maltreatment subtypes and operationalizations, as well as subgroup analyses with and without depression, and stratified by sex. In this work we show that there is little evidence for the replicability of gray matter correlates of childhood maltreatment, when adequately controlling for psychopathology. This underscores the need to focus on replicability research in mental health neuroimaging.

neuroscience↗

bioRxiv2024

Synaptogyrin-3 Prevents Cocaine Addiction and Dopamine Deficits

Synaptogyrin-3, a functionally obscure synaptic vesicle protein, interacts with vesicular monoamine and dopamine transporters, bringing together dopamine release and reuptake sites. Synaptogyrin-3 was reduced by chronic cocaine exposure in both humans and rats, and synaptogyrin-3 levels inversely correlated with motivation to take cocaine in rats. Synaptogyrin-3 overexpression in dopamine neurons reduced cocaine self-administration, decreased anxiety-like behavior, and enhanced cognitive flexibility. Overexpression also enhanced nucleus accumbens dopamine signaling and prevented cocaine-induced deficits, suggesting a putative therapeutic role for synaptogyrin-3 in cocaine use disorder.

neuroscience↗

bioRxiv2024

Brain Structural Correlates of an Impending Initial Major Depressive Episode

BackgroundNeuroimaging research has yet to elucidate, whether reported gray matter volume (GMV) alterations in major depressive disorder (MDD) exist already before the onset of the first episode. Recruitment of presently healthy individuals with a known future transition to MDD (converters) is extremely challenging but crucial to gain insights into neurobiological vulnerability. Hence, we compared converters to patients with MDD and sustained healthy controls (HC) to distinguish pre-existing neurobiological markers from those emerging later in the course of depression. MethodsCombining two clinical cohorts (n=1709), voxel-wise GMV of n=45 converters, n=748 patients with MDD, and n=916 HC were analyzed in regions-of-interest approaches. By contrasting the subgroups and considering both remission state and reported recurrence at a 2-year clinical follow-up, we stepwise disentangled effects of 1) vulnerability, 2) the acute depressive state, and 3) an initial vs. a recurrent episode. ResultsAnalyses revealed higher amygdala GMV in converters relative to HC (pTFCE-FWE=.037, d=0.447) and patients (pTFCE-FWE=.005, d=0.508), remaining significant when compared to remitted patients with imminent recurrence. Lower GMV in the dorsolateral prefrontal cortex (pTFCE-FWE<.001, d=0.188) and insula (pTFCE-FWE=.010, d=0.186) emerged in patients relative to HC but not to converters, driven by patients with acute MDD. ConclusionBy examining one of the largest available converter samples in psychiatric neuroimaging, this study allowed a first determination of neural markers for an impending initial depressive episode. Our findings suggest a temporary vulnerability, which in combination with other common risk factors might facilitate prediction and in turn improve prevention of depression.

neuroscience↗

bioRxiv2023

Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction

Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.

genetics↗

bioRxiv2023

Integrating habitat suitability modeling with gene flow improves delineation of landscape connections among African savanna elephants

Across Africa, space for conservation is sometimes limited to formally protected areas that have become progressively more isolated. There is a need for targeted conservation initiatives such as the demarcation of landscape connections, defined as areas that encompass environmental variables that promote the natural movement of individuals between populations, which can facilitate gene flow. Landscape connections can mitigate genetic isolation, genetic drift, and inbreeding, which can occur in isolated populations in protected areas. Promoting gene flow can reduce the risk of extirpation often associated with isolated populations. Here we develop and test models for identifying landscape connections among African savannah elephant (Loxodonta africana) populations by combining habitat suitability modeling with gene flow estimates across a large region including seven countries. We find a pronounced non-linear response to unsuitable habitat, consistent with previous studies showing that non-transformed habitat models are poor predictors of gene flow. We generated a landscape connections map that considers both suitable habitats based on telemetry occurrence data and gene flow estimated as the inverse of individual genetic distance, delineating areas that are important for maintaining elephant population connectivity. Our approach represents a novel framework for developing spatially and genetically informed conservation strategies for elephants and many other taxa distributed across heterogeneous and fragmented landscapes. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/554325v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1d8f639org.highwire.dtl.DTLVardef@10d31c0org.highwire.dtl.DTLVardef@13d421forg.highwire.dtl.DTLVardef@c0427c_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2023

Combining video telemetry and wearable MEG for naturalistic imaging

Neuroimaging studies have typically relied on rigorously controlled experimental paradigms to probe cognition, in which movement is restricted, primitive, an afterthought or merely used to indicate a subjects choice. Whilst powerful, these paradigms do not often resemble how we behave in everyday life, so a new generation of ecologically valid experiments are being developed. Magnetoencephalography (MEG) measures neural activity by sensing extracranial magnetic fields. It has recently been transformed from a large, static imaging modality to a wearable method where participants can move freely. This makes wearable MEG systems a prime candidate for naturalistic experiments going forward. However, these experiments will also require novel methods to capture and integrate information about complex behaviour executed during neuroimaging, and it is not yet clear how this could be achieved. Here we use video recordings of multi-limb dance moves, processed with open-source machine learning methods, to automatically identify analysis time windows of interest in concurrent wearable MEG data. In a first step, we compare a traditional, block-designed analysis of limb movements, where the times of interest are based on stimulus presentation, to an analysis pipeline based on hidden Markov model states derived from the video telemetry. Next, we show that it is possible to identify discrete modes of neuronal activity related to specific limbs and body posture by processing the participants choreographed movement in a dancing paradigm. This demonstrates the potential of combing video telemetry with mobile neuroimaging for future studies of complex and naturalistic behaviours.

neuroscience↗

bioRxiv2023

Cross-validation for the estimation of effect size generalizability in mass-univariate brain-wide association studies

IntroductionStatistical effect sizes are systematically overestimated in small samples, leading to poor generalizability and replicability of findings in all areas of research. Due to the large number of variables, this is particularly problematic in neuroimaging research. While cross-validation is frequently used in multivariate machine learning approaches to assess model generalizability and replicability, the benefits for mass-univariate brain analysis are yet unclear. We investigated the impact of cross-validation on effect size estimation in univariate voxel-based brain-wide associations, using body mass index (BMI) as an exemplary predictor. MethodsA total of n=3401 adults were pooled from three independent cohorts. Brain-wide associations between BMI and gray matter structure were tested using a standard linear mass-univariate voxel-based approach. First, a traditional non-cross-validated analysis was conducted to identify brain-wide effect sizes in the total sample (as an estimate of a realistic reference effect size). The impact of sample size (bootstrapped samples ranging from n=25 to n=3401) and cross-validation on effect size estimates was investigated across selected voxels with differing underlying effect sizes (including the brain-wide lowest effect size). Linear effects were estimated within training sets and then applied to unseen test set data, using 5-fold cross-validation. Resulting effect sizes (explained variance) were investigated. ResultsAnalysis in the total sample (n=3401) without cross-validation yielded mainly negative correlations between BMI and gray matter density with a maximum effect size of R2p=.036 (peak voxel in the cerebellum). Effects were overestimated exponentially with decreasing sample size, with effect sizes up to R2p=.535 in samples of n=25 for the voxel with the brain-wide largest effect and up to R2p=.429 for the voxel with the brain-wide smallest effect. When applying cross-validation, linear effects estimated in small samples did not generalize to an independent test set. For the largest brain-wide effect a minimum sample size of n=100 was required to start generalizing (explained variance >0 in unseen data), while n=400 were needed for smaller effects of R2p =.005 to generalize. For a voxel with an underlying null effect, linear effects found in non-cross-validated samples did not generalize to test sets even with the maximum sample size of n=3401. Effect size estimates obtained with and without cross-validation approached convergence in large samples. DiscussionCross-validation is a useful method to counteract the overestimation of effect size particularly in small samples and to assess the generalizability of effects. Train and test set effect sizes converge in large samples which likely reflects a good generalizability for models in such samples. While linear effects start generalizing to unseen data in samples of n>100 for large effect sizes, the generalization of smaller effects requires larger samples (n>400). Cross-validation should be applied in voxel-based mass-univariate analysis to foster accurate effect size estimation and improve replicability of neuroimaging findings. We provide open-source python code for this purpose (https://osf.io/cy7fp/?view_only=a10fd0ee7b914f50820b5265f65f0cdb).

neuroscience↗

bioRxiv2023

Is high-frequency activity evidence of an anterior temporal lobe network or micro-saccades?

There is renewed interest in electrical activity that extends beyond the typical electrophysiological 100 Hz bandwidth. This activity, often in the anterior temporal lobe, has been attributed to processes ranging from memory consolidation to epileptiform activity. Here, using an open-access resting state magnetoencephalography (MEG) dataset (n = 89), and a second task-based MEG dataset, we could reliably localise high-frequency power to the temporal lobes across multiple bands up to 300-400 Hz. A functional connectivity analysis of this activity revealed a robust resting state bilateral network between the temporal lobes. However, we also found robust coherence in the 100-200 and 200-300 Hz bands between source reconstructed MEG data and the electrooculography (EOG) localised to within the temporal poles. Additional denoising schemes applied to the data could reduce power localisation to the temporal poles but the topography of the functional network did not drastically alter. Whilst it is clear that this network is biological and robust to established denoising methods, we cannot definitively rule yet on whether this is of neural or myogenic origin.

neuroscience↗

bioRxiv2022

Robust Estimation of 1/f Activity Improves Oscillatory Burst Detection

Neural oscillations often occur as transient bursts with variable amplitude and frequency dynamics. Quantifying these effects is important for understanding brain-behaviour relationships, especially in continuous datasets. To robustly measure bursts, rhythmical periods of oscillatory activity must be separated from arrhythmical background 1/f activity, which is ubiquitous in electrophysiological recordings. The Better OSCillation (BOSC) framework achieves this by defining a power threshold above the estimated background 1/f activity, combined with a duration threshold. Here we introduce a modification to this approach called fBOSC which uses a spectral parametrisation tool to accurately model background 1/f activity in neural data. fBOSC (which is openly available as a MATLAB toolbox) is robust to power spectra with oscillatory peaks and can also model non-linear spectra. Through a series of simulations, we show that fBOSC more accurately models the 1/f power spectrum compared with existing methods. fBOSC was especially beneficial where power spectra contained a "knee" below [~]0.5-10 Hz, which is typical in neural data. We also found that, unlike other methods, fBOSC was unaffected by oscillatory peaks in the neural power spectrum. Moreover, by robustly modelling background 1/f activity, the sensitivity for detecting oscillatory bursts was standardised across frequencies (e.g. theta- and alpha-bands). Finally, using openly available resting state magnetoencephalography and intracranial electrophysiology datasets, we demonstrate the application of fBOSC for oscillatory burst detection in the theta-band. These simulations and empirical analyses highlight the value of fBOSC in detecting oscillatory bursts, including in datasets that are long and continuous with no distinct experimental trials. GRAPHICAL ABSRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/485674v3_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@4b0b40org.highwire.dtl.DTLVardef@cfc616org.highwire.dtl.DTLVardef@d5f09eorg.highwire.dtl.DTLVardef@1a8419b_HPS_FORMAT_FIGEXP M_FIG C_FIG To determine a power threshold for burst detection, the Better OSCillation framework (BOSC) estimates background 1/f activity by modelling neural power spectra. Here we introduce a modification, termed fBOSC, to more robustly estimate 1/f activity in situations with prominent oscillatory peaks and/or the presence of a non-linear "knee" in the power spectrum. This was shown to standardise burst detection across frequency bands in both simulated and empirical data.

neuroscience↗

bioRxiv2021

Using OPMs to measure neural activity in standing, mobile participants

Optically pumped magnetometer-based magnetoencephalography (OP-MEG) can be used to measure neuromagnetic fields while participants move in a magnetically shielded room. Head movements in previous OP-MEG studies have been up to 20 cm translation and [~]30{degrees} rotation in a sitting position. While this represents a step-change over stationary MEG systems, naturalistic head movement is likely to exceed these limits, particularly when participants are standing up. In this proof-of-concept study, we sought to push the movement limits of OP-MEG even further. Using a 90 channel (45-sensor) whole-head OP-MEG system and concurrent motion capture, we recorded auditory evoked fields while participants were: (i) sitting still, (ii) standing up and still, and (iii) standing up and making large natural head movements continuously throughout the recording - maximum translation 120 cm, maximum rotation 198{degrees}. Following pre-processing, movement artefacts were substantially reduced but not eliminated. However, upon utilisation of a beamformer, the M100 event-related field localised to primary auditory regions. Furthermore, the event-related fields from auditory cortex were remarkably consistent across the three conditions. These results suggest that a wide range of movement is possible with current OP-MEG systems. This in turn underscores the exciting potential of OP-MEG for recording neural activity during naturalistic paradigms that involve movement (e.g. navigation), and for scanning populations who are difficult to study with stationary MEG (e.g. young children).

neuroscience↗

bioRxiv2021

Magnetic Field Mapping and Correction for Moving OP-MEG

BackgroundOptically pumped magnetometers (OPMs) have made moving, wearable magnetoencephalography (MEG) possible. The OPMs typically used for MEG require a low background magnetic field to operate, which is achieved using both passive and active magnetic shielding. However, the background magnetic field is never truly zero Tesla, and so the field at each of the OPMs changes as the participant moves. This leads to position and orientation dependent changes in the measurements, which manifest as low frequency artefacts in MEG data. ObjectiveWe modelled the spatial variation in the magnetic field and used the model to predict the movement artefact found in a dataset. MethodsWe demonstrate a method for modelling this field with a triaxial magnetometer, then showed that we can use the same technique to predict the movement artefact in a real OPM-based MEG (OP-MEG) dataset. ResultsUsing an 86-channel OP-MEG system, we found that this modelling method maximally reduced the power spectral density of the data by 26.2 {+/-} 0.6 dB at 0 Hz, when applied over 5 s non-overlapping windows. ConclusionThe magnetic field inside our state-of-the art magnetically shielded room can be well described by low-order spherical harmonic functions. We achieved a large reduction in movement noise when we applied this model to OP-MEG data. SignificanceReal-time implementation of this method could reduce passive shielding requirements for OP-MEG recording and allow the measurement of low-frequency brain activity during natural participant movement.

neuroscience↗

bioRxiv2020

Modelling optically pumped magnetometerinterference as a mean (magnetic) field

Here we propose that much of the magnetic interference observed when using optically pumped magnetometers can be modeled spatially as a mean (magnetic) field. We show that this approximation reduces sensor level variability and substantially improves statistical power. This model does not require knowledge of the underlying neuroanatomy nor the sensor positions. It only needs information about the sensor orientation. Due to the models low rank there is little risk of removing substantial neural signal. However, we provide a framework to assess this risk for any sensor number, design or subject neuroanatomy. We find that the risk of unintentionally removing neural signal is reduced when multi-axis recordings are performed. We validated the method using a binaural auditory evoked response paradigm and demonstrated that the mean field correction increases reconstructed SNR in relevant brain regions in both the spatial and temporal domain. Considering the models simplicity and efficacy, we suggest that this mean field correction can be a powerful preprocessing step for arrays of optically pumped magnetometers.

neuroscience↗

bioRxiv2020

Field efficacy of guppies and pyriproxyfen (Sumilarv(R) 2MR) combined with community engagement on dengue vectors in Cambodia: a randomized controlled trial

Evidence on the effectiveness of low-cost, sustainable biological vector control tools for Aedes mosquitoes is limited. Therefore, the purpose of this trial is to estimate the impact of guppy fish, in combination with the use of the larvicide Pyriproxyfen (Sumilarv(R) 2MR), and Communication for Behavioral Impact (COMBI) activities to reduce entomological indices in Cambodia. In this cluster randomized, controlled superiority trial, 30 clusters comprising of one or more villages each (with approximately 170 households) will be allocated, in a 1:1:1 ratio, to receive either a) three interventions (guppies, Sumilarv(R) 2MR, and COMBI activities), b) two interventions (guppies and COMBI activities), or c) control (standard vector control). Households were invited to participate, and entomology surveys among 40 randomly selected households per cluster were carried out quarterly. The primary outcome was the population density of adult female Aedes mosquitoes (i.e. number per house) trapped using adult resting collections. Secondary outcome measures include the House index, Container index, Breteau index, Pupae Per House, Pupae Per Person, mosquito infection rate, guppy fish coverage, Sumilarv(R) 2MR coverage, and percentage of respondents with knowledge about Aedes mosquitoes causing dengue. In the primary analysis, adult female Aedes density and mosquito infection rates was aggregated over follow-up time points to give a single rate per cluster. This was analyzed by negative binomial regression, yielding density ratios. The number of Aedes females was reduced by roughly half compared to the control in both the guppy and PPF arm (Density Ratio (DR)=0.54 [95% CI 0.34-0.85], p=0.0073), and guppy arm (DR=0.49 [95% CI 0.31-0.77], p=0.0021). The extremely low cost of including guppy rearing in community-based health structures along with the effectiveness demonstrated suggest guppies should be considered as a vector control tool as long as the benefits outweigh any potential environmental concerns. PPF was also highly accepted and preferred over current vector control tools used in Cambodia, however product costs and availability are still unknown. Author SummaryDengue is one of the most rapidly spreading mosquito-borne viral diseases in the world, is caused by bites of infected Aedes mosquitoes, and can sometimes lead to death. Cambodia has one of the highest per-capita incidence rates in Asia. Without a cure or routinely available efficacious vaccine, dengue control relies largely on reduction and avoidance of mosquitoes. In Cambodia, dengue mosquito control activities are focused on larviciding with temephos and pyrethroid based adulticide sprays to which Aedes have been shown to be increasingly resistant. This study was designed to evaluate novel biological vector control tools (guppy fish and a controlled release larvicidal matrix) utilizing an integrated vector management approach with community-based methods tailored to the local context. The results indicate that the tools resulted in a statistically significant reduction in immature and adult Aedes mosquito density. The interventions were accepted by and communities were willing to pay for them. The results suggest guppies are an ideal vector control tool as long as the benefits outweigh any potential environmental concerns. PPF was also highly accepted and preferred over current vector control tools used in Cambodia, however product costs and availability are still unknown.

ecology↗

bioRxiv2019

Local human movement patterns and land use impact exposure to zoonotic malaria in Malaysian Borneo

Human movement into insect vector and wildlife reservoir habitats determines zoonotic disease risks; however, few data are available to quantify the impact of land use on pathogen transmission. Here, we utilise GPS tracking devices and novel applications of ecological methods to develop fine-scale models of human space use relative to land cover to assess exposure to the zoonotic malaria Plasmodium knowlesi in Malaysian Borneo. Combining data with spatially explicit models of mosquito biting rates, we demonstrate the role of individual heterogeneities in local space use in disease exposure. At a community level, our data indicate that areas close to both secondary forest and houses have the highest probability of human P. knowlesi exposure, providing quantitative evidence for the importance of ecotones. Despite higher biting rates in forests, incorporating human movement space use into exposure estimates illustrates the importance of intensified interactions between pathogens, insect vectors and people around habitat edges.

ecology↗