Cross-Sectional Association of \u3ci\u3eToxoplasma gondii\u3c/i\u3e Exposure with BMI and Diet in US Adults

Toxoplasmosis gondii exposure has been linked to increased impulsivity and risky behaviors, which has implications for eating behavior. Impulsivity and risk tolerance is known to be related with worse diets and a higher chance of obesity. There is little known, however, about the independent link between Toxoplasma gondii (T. gondii) exposure and diet-related outcomes. Using linear and quantile regression, we estimated the relationship between T. gondii exposure and BMI, total energy intake (kcal), and diet quality as measured by the Health Eating Index-2015 (HEI) among 9,853 adults from the 2009–2014 National Health and Nutrition Examination Survey. Previous studies have shown different behavioral responses to T. gondii infection among males and females, and socioeconomic factors are also likely to be important as both T. gondii and poor diet are more prevalent among U.S. populations in poverty. We therefore measured the associations between T. gondii and diet-related outcomes separately for men and women and for respondents in poverty. Among females \u3c 200% of the federal poverty level Toxoplasmosis gondii exposure was associated with a higher BMI by 2.0 units (95% CI [0.22, 3.83]) at median BMI and a lower HEI by 5.05 units (95% CI [-7.87, -2.24]) at the 25th percentile of HEI. Stronger associations were found at higher levels of BMI and worse diet quality among females. No associations were found among males. Through a detailed investigation of mechanisms, we were able to rule out T. gondii exposure from cat ownership, differing amounts of meat, and drinking water source as potential confounding factors; environmental exposure to T. gondii as well as changes in human behavior due to parasitic infection remain primary mechanisms

Strong trait correlation and phylogenetic signal in North American ground beetle (Carabidae) morphology

Functional traits mediate species’ responses to, and roles within, their environment and are constrained by evolutionary history. While we have a strong understanding of trait evolution for macrotaxa such as birds and mammals, our understanding of invertebrates is comparatively limited. Here, we address this gap in North American beetles with a sample of ground beetles (Carabidae), leveraging a large-scale collection and digitization effort by the National Ecological Observatory Network (NEON). For 154 ground beetle species, we measured seven morphological traits, which we placed into a recently developed effect–response framework that characterizes traits by how they predict species’ effects on their ecosystems or responses to environmental stressors. We then used cytochrome oxidase 1 sequences from the same specimens to generate a phylogeny and tested the evolutionary tempo and mode of the traits. We found strong phylogenetic signal in, and correlations among, ground beetle morphological traits. These results indicate that, for these species, beetle body shape trait evolution is constrained, and phylogenetic inertia is a stronger driver of beetle traits than (recent) environmental responses. Strong correlations among effect and response traits suggest that future environmental drivers are likely to affect both ecological composition and functioning in these beetles

Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae)

Species Distribution Modelling (SDM) determines habitat suitability of a species across geographic areas using macro-climatic variables; however, micro-habitats can buffer or exacerbate the influence of macro-climatic variables, requiring links between physiology and species persistence. Experimental approaches linking species physiology to micro-climate are complex, time consuming and expensive. E.g., what combination of exposure time and temperature is important for a species thermal tolerance is difficult to judge a priori. We tackled this problem using an active learning approach that utilized machine learning methods to guide thermal tolerance experimental design for three kissing-bug species: Triatoma infestans, Rhodnius prolixus, and Panstrongylus megistus (Hemiptera: Reduviidae: Triatominae), vectors of the parasite causing Chagas disease. As with other pathogen vectors, triatomines are well known to utilize micro-habitats and the associated shift in microclimate to enhance survival. Using a limited literature-collected dataset, our approach showed that temperature followed by exposure time were the strongest predictors of mortality; species played a minor role, and life stage was the least important. Further, we identified complex but biologically plausible nonlinear interactions between temperature and exposure time in shaping mortality, together setting the potential thermal limits of triatomines. The results from this data led to the design of new experiments with laboratory results that produced novel insights of the effects of temperature and exposure for the triatomines. These results, in turn, can be used to better model micro-climatic envelope for the species. Here we demonstrate the power of an active learning approach to explore experimental space to design laboratory studies testing species thermal limits. Our analytical pipeline can be easily adapted to other systems and we provide code to allow practitioners to perform similar analyses. Not only does our approach have the potential to save time and money: it can also increase our understanding of the links between species physiology and climate, a topic of increasing ecological importance.Centro de Estudios Parasitológicos y de Vectore

Domestic horses within the Maya biosphere reserve: A possible threat to the Central American tapir (Tapirus bairdii)

The Central American tapir (Tapirus bairdii) is the largest herbivore in the Neotropics classified as “endangered.” It has been proposed that Equine Infectious Anemia virus (EIA) is a disease of horses with potential to lead to further decline of T. bairdii populations. In this study, we used domestic horses as sentinels for EIA in the Maya Biosphere Reserve in Guatemala. In total, 40 % (13) horses tested were seropositive to EIA. This study may inform wildlife management strategies inside protected areas by considering the threat from incursions of domestic animals inside core areas of natural reserves.El tapir centroamericano (Tapirus bairdii) es el herbívoro más grande del Neotrópico clasificado “en peligro de extinción”. Ha sido propuesto que la Anemia Infecciosa Equina (AIE) es una enfermedad de caballos con potencial de provocar una declinación de las poblaciones de T. bairdii. En este estudio utilizamos caballos domésticos como centinelas para AIE en la Reserva de la Biosfera Maya en Guatemala. En total, el 40 % (13) caballos evaluados fueron seropositivos a AIE. Este estudio puede orientar las estrategias de manejo de áreas protegidas, considerando la amenaza de incursiones de animales domésticos en zonas núcleo de reservas naturales

Using host traits to predict reservoir host species of rabies virus

Wildlife are important reservoirs for many pathogens, yet the role that different species play in pathogen maintenance frequently remains unknown. This is the case for rabies, a viral disease of mammals. While Carnivora (carnivores) and Chiroptera (bats) are the canonical mammalian orders known to be responsible for the maintenance and onward transmission of rabies Lyssavirus (RABV), the role of most species within these orders remains unknown and is continually changing as a result of contemporary host shifting. We combined a trait-based analytical approach with gradient boosting machine learning models to identify physiological and ecological host features associated with being a reservoir for RABV. We then used a cooperative game theory approach to determine species-specific traits associated with known RABV reservoirs. Being a carnivore reservoir for RABV was associated with phylogenetic similarity to known RABV reservoirs, along with other traits such as having larger litters and earlier sexual maturity. For bats, location in the Americas and geographic range were the most important predictors of RABV reservoir status, along with having a large litter. Our models identified 44 carnivore and 34 bat species that are currently not recognized as RABV reservoirs, but that have trait profiles suggesting their capacity to be or become reservoirs. Further, our findings suggest that potential reservoir species among bats and carnivores occur both within and outside of areas with current RABV circulation. These results show the ability of a trait-based approach to detect potential reservoirs of infection and could inform rabies control programs and surveillance efforts by identifying the types of species and traits that facilitate RABV maintenance and transmission

Cross‐species transmission and evolutionary dynamics of canine distemper virus during a spillover in African lions of Serengeti National Park

The outcome of pathogen spillover from a reservoir to a novel host population can range from a “dead‐end” when there is no onward transmission in the recipient population, to epidemic spread and even establishment in new hosts. Understanding the evolutionary epidemiology of spillover events leading to discrete outcomes in novel hosts is key to predicting risk and can lead to a better understanding of mechanisms of emergence. Here we use a Bayesian phylodynamic approach to examine cross‐species transmission and evolutionary dynamics during a canine distemper virus spillover event causing clinical disease and population decline in an African lion population (Panthera leo) in the Serengeti Ecological Region between 1993 and 1994. Using 21 near‐complete viral genomes from four species we found that this large‐scale outbreak was likely ignited by a single cross‐species spillover event from a canid reservoir to non‐canid hosts less than one year before disease detection and explosive spread of CDV in lions. Cross‐species transmission from other non‐canid species likely fueled the high prevalence of CDV across spatially structured lion prides. Multiple lines of evidence suggest that spotted hyenas (Crocuta crocuta) could have acted as the proximate source of CDV exposure in lions. We report thirteen nucleotide substitutions segregating CDV strains found in canids and non‐canids. Our results are consistent with the hypothesis that virus evolution played a role in CDV emergence in non‐canid hosts following spillover during the outbreak, and suggests that host barriers to clinical infection can limit outcomes of CDV spillover in novel host species

On the Effect of Nb on the Microstructure and Properties of Next Generation Polycrystalline Powder Metallurgy Ni-Based Superalloys

Abstract The effect of Nb on the properties and microstructure of two novel powder metallurgy (P/M) Ni-based superalloys was evaluated, and the results critically compared with the Rolls-Royce alloy RR1000. The Nb-containing alloy was found to exhibit improved tensile and creep properties as well as superior oxidation resistance compared with both RR1000 and the Nb-free variant tested. The beneficial effect of Nb on the tensile and creep properties was due to the microstructures obtained following the post-solution heat treatments, which led to a higher γ′ volume fraction and a finer tertiary γ′ distribution. In addition, an increase in the anti-phase-boundary energy of the γ′ phase is also expected with the addition of Nb, further contributing to the strength of the material. However, these modifications in the γ′ distribution detrimentally affect the dwell fatigue crack-growth behavior of the material, although this behavior can be improved through modified heat treatments. The oxidation resistance of the Nb-containing alloy was also enhanced as Nb is believed to accelerate the formation of a defect-free Cr2O3 scale. Overall, both developmental alloys, with and without the addition of Nb, were found to exhibit superior properties than RR1000.This work was supported by the Rolls-Royce/EPSRC Strategic Partnership under EP/H022309/1, EP/H500375/1 and EP/ M005607/1

Machine learning in molecular ecology

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