Individual-Level Antibody Dynamics Reveal Potential Drivers of Influenza A Seasonality in Wild Pig Populations

Swine are important in the ecology of influenza A virus (IAV) globally. Understanding the ecological role of wild pigs in IAV ecology has been limited because surveillance in wild pigs is often for antibodies (serosurveillance) rather than IAVs, as in humans and domestic swine. As IAV antibodies can persist long after an infection, serosurveillance data are not necessarily indicative of current infection risk. However, antibody responses to IAV infections cause a predictable antibody response, thus time of infection can be inferred from antibody levels in serological samples, enabling identification of risk factors of infection at estimated times of infection. Recent work demonstrates that these quantitative antibody methods (QAMs) can accurately recover infection dates, even when individual-level variation in antibody curves is moderately high. Also, the methodology can be implemented in a survival analysis (SA) framework to reduce bias from opportunistic sampling. Here we integrated QAMs and SA and applied this novel QAM–SA framework to understand the dynamics of IAV infection risk in wild pigs seasonally and spatially, and identify risk factors. We used national-scale IAV serosurveillance data from 15 US states. We found that infection risk was highest during January– March (54% of 61 estimated peaks), with 24% of estimated peaks occurring from May to July, and some low-level of infection risk occurring year-round. Time-varying IAV infection risk in wild pigs was positively correlated with humidity and IAV infection trends in domestic swine and humans, and did not show wave-like spatial spread of infection among states, nor more similar levels of infection risk among states with more similar meteorological conditions. Effects of host sex on IAV infection risk in wild pigs were generally not significant. Because most of the variation in infection risk was explained by state-level factors or infection risk at long-distances, our results suggested that predicting IAV infection risk in wild pigs is complicated by local ecological factors and potentially long-distance translocation of infection. In addition to revealing factors of IAV infection risk in wild pigs, our framework is broadly applicable for quantifying risk factors of disease transmission using opportunistic serosurveillance sampling, a common methodology in wildlife disease surveillance. Future research on the factors that determine individual-level antibody kinetics will facilitate the design of serosurveillance systems that can extract more accurate estimates of time-varying disease risk from quantitative antibody data

A model for leveraging animal movement to understand spatio-temporal disease dynamics

The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable model that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible model ‘Movement-driven modelling of spatio-temporal infection risk’ (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology

Deriving spatially explicit direct and indirect interaction networks from animal movement data

Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems [“GPS”]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous-time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions—individuals occurring at the same location, but at different times—while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease-relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30-min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM-Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine-scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer–resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers

Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace-of-life

Pathogen transmission depends on host density, mobility and contact. These components emerge from host and pathogen movements that themselves arise through interactions with the surrounding environment. The environment, the emergent host and pathogen movements, and the subsequent patterns of density, mobility and contact form an ‘epidemiological landscape’ connecting the environment to specific locations where transmissions occur. Conventionally, the epidemiological landscape has been described in terms of the geographical coordinates where hosts or pathogens are located. We advocate for an alternative approach that relates those locations to attributes of the local environment. Environmental descriptions can strengthen epidemiological forecasts by allowing for predictions even when local geographical data are not available. Environmental predictions are more accessible than ever thanks to new tools from movement ecology, and we introduce a ‘movement-pathogen pace of life’ heuristic to help identify aspects of movement that have the most influence on spatial epidemiology. By linking pathogen transmission directly to the environment, the epidemiological landscape offers an efficient path for using environmental information to inform models describing when and where transmission will occur

Patterns of HER-2/neu Amplification and Overexpression in Primary and Metastatic Breast Cancer

Background: Only 25% of patients with HER-2/neu-positive metastatic breast tumors respond favorably to trastuzamab (Herceptin) treatment. We hypothesized that a high failure rate of patients on trastuzamab could result if some of the metastases were HER-2 negative and these metastases ultimately determine the course of the disease. Methods: We used tissue microarrays (TMAs) containing four samples each from 196 lymph node-negative primary tumors, 196 lymph node-positive primary tumors, and three different lymph node metastases from each lymph node-positive tumor to estimate HER-2 gene amplification by fluorescence in situ hybridization (FISH) and Her-2 protein overexpression by immunohistochemistry (IHC). Results: FISH and IHC analyses gave the same result with respect to HER-2 status for 93.7% of the tissues contained in the TMAs. Tissue samples were, therefore, considered to be HER-2 positive if they were positive for either HER-2 DNA amplification or Her-2 protein expression and HER-2 negative if both FISH and IHC gave a negative result. The HER-2 status of lymph node-positive primary tumors was maintained in the majority of their metastases. For HER-2-positive primary tumors, 77% (95% confidence interval [CI] = 59% to 90%) had entirely HER-2-positive metastases, 6.5% (95% CI = 8% to 21%) had entirely HER-2-negative metastases, and 16.3% (95% CI = 5% to 34%) had a mixture of HER-2-positive and HER-2-negative metastases. For HER-2-negative primary tumors, 95% (95% CI = 88% to 98%) had metastases that were entirely negative for HER-2. Conclusions: Our data suggest that differences in HER-2 expression between primary tumors and their lymph node metastases cannot explain the high fraction of nonresponders to trastuzamab therap

Predicting functional responses in agro-ecosystems from animal movement data to improve management of invasive pests

Functional responses describe how changing resource availability affects con- sumer resource use, thus providing a mechanistic approach to prediction of the invasibility and potential damage of invasive alien species (IAS). However, functional responses can be context dependent, varying with resource characteristics and availability, consumer attributes, and environmental variables. Identifying context dependencies can allow invasion and damage risk to be predicted across different ecoregions. Understanding how ecological factors shape the functional response in agro-ecosystems can improve predictions of hotspots of highest impact and inform strategies to mitigate damage across locations with varying crop types and avail- ability. We linked heterogeneous movement data across different agro-ecosystems to predict ecologically driven variability in the functional responses. We applied our approach to wild pigs (Sus scrofa), one of the most successful and detrimental IAS worldwide where agricultural resource depredation is an important driver of spread and establishment. We used continental- scale movement data within agro-ecosystems to quantify the functional response of agricul- tural resources relative to availability of crops and natural forage. We hypothesized that wild pigs would selectively use crops more often when natural forage resources were low. We also examined how individual attributes such as sex, crop type, and resource stimulus such as dis- tance to crops altered the magnitude of the functional response. There was a strong agricul- tural functional response where crop use was an accelerating function of crop availability at low density (Type III) and was highly context dependent. As hypothesized, there was a reduced response of crop use with increasing crop availability when non-agricultural resources were more available, emphasizing that crop damage levels are likely to be highly heterogeneous depending on surrounding natural resources and temporal availability of crops. We found sig- nificant effects of crop type and sex, with males spending 20% more time and visiting crops 58% more often than females, and both sexes showing different functional responses depend- ing on crop type. Our application demonstrates how commonly collected animal movement data can be used to understand context dependencies in resource use to improve our under- standing of pest foraging behavior, with implications for prioritizing spatiotemporal hotspots of potential economic loss in agro-ecosystems

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

Production of Embryonic and Fetal-Like Red Blood Cells from Human Induced Pluripotent Stem Cells

We have previously shown that human embryonic stem cells can be differentiated into embryonic and fetal type of red blood cells that sequentially express three types of hemoglobins recapitulating early human erythropoiesis. We report here that we have produced iPS from three somatic cell types: adult skin fibroblasts as well as embryonic and fetal mesenchymal stem cells. We show that regardless of the age of the donor cells, the iPS produced are fully reprogrammed into a pluripotent state that is undistinguishable from that of hESCs by low and high-throughput expression and detailed analysis of globin expression patterns by HPLC. This suggests that reprogramming with the four original Yamanaka pluripotency factors leads to complete erasure of all functionally important epigenetic marks associated with erythroid differentiation regardless of the age or the tissue type of the donor cells, at least as detected in these assays. The ability to produce large number of erythroid cells with embryonic and fetal-like characteristics is likely to have many translational applications

Enhanced Extinction of Aversive Memories by High-Frequency Stimulation of the Rat Infralimbic Cortex

Electrical stimulation of the rodent medial prefrontal cortex (mPFC), including the infralimbic cortex (IL), immediately prior to or during fear extinction training facilitates extinction memory. Here we examined the effects of high-frequency stimulation (HFS) of the rat IL either prior to conditioning or following retrieval of the conditioned memory, on extinction of Pavlovian fear and conditioned taste aversion (CTA). IL-HFS applied immediately after fear memory retrieval, but not three hours after retrieval or prior to conditioning, subsequently reduced freezing during fear extinction. Similarly, IL-HFS given immediately, but not three hours after, retrieval of a CTA memory reduced aversion during extinction. These data indicate that HFS of the IL may be an effective method for reducing both learned fear and learned aversion