Questionnaires exploring People's Experiences of Imagined Sensations

Chikungunya virus (CHIKV) is a positive sense RNA Alphavirus that continues to pose major public health threats throughout the world. CHIKV is primarily transmitted via the Aedes genus mosquito; however, it has also exhibited transmission routes via blood transfusion and vertical transmission (mother to child). With only one approved vaccine thus far and no approved medicines, early detection is crucial in mitigating CHIKV outbreaks. Here, we designed and evaluated a sensitive and specific CHIKV diagnostic using reverse transcription-recombinase aided amplification (RT-RAA) coupled lateral flow strip detection (LFD) targeting a highly conserved region of the CHIKV E1 gene. Our results demonstrate that using our simple sample preparation reagent (TNA-Cifer-E) can inactivate live CHIKV in two minutes at room temperature, whilst also sustaining viable viral RNA. Our specificity analysis demonstrates that the Iso-CHIKV-Dx does not detect any closely related Alphaviruses nor any of the common co-circulating Flaviviruses. Proof-of-concept evaluation using urine spiked with CHIKV exhibited that in CHIKV-infected urine samples, this Iso-CHIKV-Dx can detect as low as 570 copies/μL of CHIKV RNA in 30 minutes under isothermal conditions. Contrary to conventional RT-qPCR, our Iso-CHIKV-Dx does not require expensive machinery, advanced instrumentation or extensively trained personnel. Further performance comparisons also show that this Iso-CHIKV-Dx is four times faster than conventional RNA isolation and RT-qPCR. As such, pre-clinical evaluation demonstrates that this Iso-CHIKV-Dx has the potential to act as a robust, point of care CHIKV diagnostic that could prove to be highly beneficial in place of, or in the absence of RT-qPCR.

Individual plant size often determines the vital rates of growth, survival, and reproduction. However, size can be measured in several ways (e.g., height, biomass, leaf length). There is no consensus on the best size metric for modelling vital rates in plants. Demographic datasets are expanding in geographic extent, leading to choices about how to represent size for the same species in multiple ecological contexts. If the choice of size variable varies among locations, inter-population comparative demography increases in complexity. Here, we present a framework to perform size metric selection in large-scale demographic studies. We highlight potential pitfalls and suggest methods applicable to diverse study organisms. We assessed the performance of five different size metrics for the perennial herb Plantago lanceolata across 55 populations on three continents within its native and non-native ranges, using the spatially replicated demographic dataset PlantPopNet. We compared the performance of each candidate size metric for four vital rates (growth, survival, flowering probability, and reproductive output) using generalized linear mixed models. We ranked the candidate size metrics based on their overall performance (highest generalized R2) and homogeneity of performance across populations (lowest total magnitude of, and variance in, population-level error). While all size variables performed well for modelling vital rates, the number of leaves (modelled as a discrete variable, without transformation) was selected as the best size metric, followed by leaf length. We show how to interrogate potential trade-offs between overall explanatory power and homogeneity of predictions across populations in any organism. Synthesis: Size is an important determinant of vital rates. Using a dataset of unprecedented spatial extent, we find a) consistent size-based models of growth, survival, and reproduction across native and non-native populations of this cosmopolitan plant species and b) that several tested size metrics perform similarly well. This is encouraging for large-scale demographic studies and for comparative projects using different size metrics, as they may be robust to this methodological difference.

During sleep, behavioral responsiveness to external stimuli is decreased. This classical definition of sleep has been applied effectively across the animal kingdom to identify this common behavioral state in a growing list of creatures, from mammals to invertebrates. Yet it remains unclear whether decreased behavioral responsiveness during sleep is necessarily associated with decreased responsiveness in brain activity, especially in insects. Here, we perform long-term multichannel electrophysiology in tethered Drosophila melanogaster flies exposed continuously to repetitive visual stimuli. Flies were still able to sleep under these visual stimulation conditions, as determined by traditional immobility duration criteria for the field. Interestingly, we did not find any difference between responses to repetitive visual stimuli during sleep compared to wake when we recorded local field potentials (LFP) across a transect of the fly brain from optic lobes to the central brain. However, we did find LFP responses to be altered when visual stimuli were variable and of lower probability, especially in the central brain. Central brain responses to less predictable or ‘deviant’ stimuli were lower during the deepest stage of sleep, a time of quiescence characterized by more regular proboscis extensions. This shows that the sleeping fly brain processes low-probability visual stimuli in a different way than more repeated stimuli, and presents Drosophila as a promising model for studying the potential role of sleep in regulating predictive processing.

In this study, we examined the gene expression profiles of Sf9 insect cells following infection with baculovirus, utilizing reverse transcription quantitative PCR (RT-qPCR) to analyze how the host cellular machinery responds at the transcriptional level to viral invasion. Our findings highlight the intricate regulatory mechanisms in place during virus replication. To further understand the relationship between the host response and viral propagation, we also quantified the dynamics of baculovirus replication within the Sf9 cells using quantitative PCR (qPCR). This dual approach provides a comprehensive view of both the efficiency of viral replication and the accompanying host transcriptional changes.

These datasets represent a Human Industrial Footprint (HIF) index map and an Ecological Intactness Index (EII) map for Australia circa 2020-2024. The datasets are distributed in raster format (.tif) and have a spatial resolution of 100 m, mapped on an Australian Albers Equal Area projection (EPSG:3577). The HIF was created by incorporating 16 nationally relevant pressure layers, also part of the dataset. The pressures used to compute the HIF were 1) intensive land uses, 2) buildings, 3) mining and quarrying, 4) human population density, 5) croplands, 6) pasturelands, 7) forestry plantations, 8) reservoirs and large dams, 9) farm dams, 10) roads, 11) railways, 12) energy transmission lines, 13) oil pipelines, 14) gas pipelines, 15) hiking trails, and 16)  navigable waterways. Each pressure layer was assigned a relative score between 0 and 10 to make them comparable. The scored (scaled) pressure layers were then summed to obtain the final HIF map.The HIF was used to derive the Ecological Intactness Index (EII). The EII is calculated using the HIF, with the intactness index value for each cell parameterised to: a) be proportional to habitat area when there is no habitat fragmentation; b) decline mono-tonically as fragmentation increases, and be sensitive to both the number of nearby patches and the separation between patches, and (c) to be proportional to habitat quality for a given total area of habitat and degree of fragmentation.In the pressure layer folder, native and modified pasturelands are merged in the "pastures" pressure layer and paved and unpaved roads are in the "roads" layer.AcknowledgementsThis research was funded by The Wilderness Society.ContactFurther queries regarding these datasets can be directed to Ruben Venegas ([email protected]) and James Watson ([email protected]).

These datasets represent a Human Industrial Footprint (HIF) index map and an Ecological Intactness Index (EII) map for Australia circa 2020-2024. The datasets are distributed in raster format (.tif) and have a spatial resolution of 100 m, mapped on an Australian Albers Equal Area projection (EPSG:3577). The HIF was created by incorporating 16 nationally relevant pressure layers, also part of the dataset. The pressures used to compute the HIF were 1) intensive land uses, 2) buildings, 3) mining and quarrying, 4) human population density, 5) croplands, 6) pasturelands, 7) forestry plantations, 8) reservoirs and large dams, 9) farm dams, 10) roads, 11) railways, 12) energy transmission lines, 13) oil pipelines, 14) gas pipelines, 15) hiking trails, and 16)  navigable waterways. Each pressure layer was assigned a relative score between 0 and 10 to make them comparable. The scored (scaled) pressure layers were then summed to obtain the final HIF map.The HIF was used to derive the Ecological Intactness Index (EII). The EII is calculated using the HIF, with the intactness index value for each cell parameterised to: a) be proportional to habitat area when there is no habitat fragmentation; b) decline mono-tonically as fragmentation increases, and be sensitive to both the number of nearby patches and the separation between patches, and (c) to be proportional to habitat quality for a given total area of habitat and degree of fragmentation.In the pressure layer folder, native and modified pasturelands are merged in the "pastures" pressure layer and paved and unpaved roads are in the "roads" layer.The code to create these maps is also available through this repository.  The code is an end‑to‑end GRASS GIS pipeline to rebuild the Human Industrial Footprint Index for continental Australia on a 100 m grid in Albers Australia Equal Area (EPSG:3577). It generates 16 pressure layers, applies hierarchical priority (Urban > Mining > Crops >Pasture), scales each 0–10, and exports individual layers plus the summed index as Cloud‑Optimised GeoTIFFs (COGs).AcknowledgementsThis research was funded by The Wilderness Society.ContactFurther queries regarding these datasets can be directed to Ruben Venegas ([email protected]) and James Watson ([email protected]).

These datasets represent a Human Industrial Footprint (HIF) index map and an Ecological Intactness Index (EII) map for Australia circa 2020-2024. The datasets are distributed in raster format (.tif) and have a spatial resolution of 100 m, mapped on an Australian Albers Equal Area projection (EPSG:3577). The HIF was created by incorporating 16 nationally relevant pressure layers, also part of the dataset. The pressures used to compute the HIF were 1) intensive land uses, 2) buildings, 3) mining and quarrying, 4) human population density, 5) croplands, 6) pasturelands, 7) forestry plantations, 8) reservoirs and large dams, 9) farm dams, 10) roads, 11) railways, 12) energy transmission lines, 13) oil pipelines, 14) gas pipelines, 15) hiking trails, and 16)  navigable waterways. Each pressure layer was assigned a relative score between 0 and 10 to make them comparable. The scored (scaled) pressure layers were then summed to obtain the final HIF map.The HIF was used to derive the Ecological Intactness Index (EII). The EII is calculated using the HIF, with the intactness index value for each cell parameterised to: a) be proportional to habitat area when there is no habitat fragmentation; b) decline mono-tonically as fragmentation increases, and be sensitive to both the number of nearby patches and the separation between patches, and (c) to be proportional to habitat quality for a given total area of habitat and degree of fragmentation.In the pressure layer folder, native and modified pasturelands are merged in the "pastures" pressure layer and paved and unpaved roads are in the "roads" layer.The code to create these maps is also available through this repository.  The code is an end‑to‑end GRASS GIS pipeline to rebuild the Human Industrial Footprint Index for continental Australia on a 100 m grid in Albers Australia Equal Area (EPSG:3577). It generates 16 pressure layers, applies hierarchical priority (Urban > Mining > Crops >Pasture), scales each 0–10, and exports individual layers plus the summed index as Cloud‑Optimised GeoTIFFs (COGs).AcknowledgementsThis research was funded by The Wilderness Society.ContactFurther queries regarding these datasets can be directed to Ruben Venegas ([email protected]) and James Watson ([email protected]).

Although mosquitoes can have innate preferences for particular blood-meal hosts, their realised feeding patterns on different host species can be modified under climate and land use change, with implications for disease spread. It is therefore important to understand the niche breadth of vectors and to what extent shifts in feeding patterns are predictable. We investigated global shifts in feeding patterns among different functional and taxonomic groups of host species for six prominent disease-vectoring mosquitoes.Time period: 2000–2019. Major taxa studied: six disease-vectoring mosquito species: Aedes aegypti, Ae. albopictus, Anopheles funestus, An. gambiae, Culex pipiens, and Cx. quinquefasciatus. Focusing on blood meal studies that used universal molecular methods, we compiled evidence from >15,600 blood-meals for the six mosquito species. We estimated mosquito’s host niche breadth, and we used hierarchical Dirichlet regression models to investigate shifts in feeding patterns in relation to human and livestock density, land use, and climate gradients. We estimated host ranges of 179–321 species for each of the two Culex mosquitoes and 24 - 65 species for Aedes mosquitoes, comprising considerably broader host niche breadths than previously anticipated. We found some evidence that shifts in feeding patterns among different host functional and taxonomic groups were associated with environmental conditions such as temperature and livestock density, while our results also demonstrate that, with the currently available evidence, global predictions of shifts in mosquito feeding patterns are challenged by considerable uncertainty. Our global metaanalysis afforded first insights into the shifts of feeding patterns in variable environments, suggesting that host choice is not a simple function of host availability, but contingent on other environmental drivers. Improving resolution and consistency of data gathering and reporting will improve the precision of how blood-meal studies can inform us of present and potential risks of pathogen transmission events.

Table S1. Gene ontology term enrichment. Significantly (FDR < 0.05) up-regulated ontologies in longissimus lumborum muscle of bitter mix (combination of caffeine, grape and gentian extracts) fed pigs.