Flexible Modeling of Epidemics with an Empirical Bayes Framework

Seasonal influenza epidemics cause consistent, considerable, widespread loss annually in terms of economic burden, morbidity, and mortality. With access to accurate and reliable forecasts of a current or upcoming influenza epidemic’s behavior, policy makers can design and implement more effective countermeasures. This past year, the Centers for Disease Control and Prevention hosted the “Predict the Influenza Season Challenge”, with the task of predicting key epidemiological measures for the 2013–2014 U.S. influenza season with the help of digital surveillance data. We developed a framework for in-season forecasts of epidemics using a semiparametric Empirical Bayes framework, and applied it to predict the weekly percentage of outpatient doctors visits for influenza-like illness, and the season onset, duration, peak time, and peak height, with and without using Google Flu Trends data. Previous work on epidemic modeling has focused on developing mechanistic models of disease behavior and applying time series tools to explain historical data. However, tailoring these models to certain types of surveillance data can be challenging, and overly complex models with many parameters can compromise forecasting ability. Our approach instead produces possibilities for the epidemic curve of the season of interest using modified versions of data from previous seasons, allowing for reasonable variations in the timing, pace, and intensity of the seasonal epidemics, as well as noise in observations. Since the framework does not make strict domain-specific assumptions, it can easily be applied to some other diseases with seasonal epidemics. This method produces a complete posterior distribution over epidemic curves, rather than, for example, solely point predictions of forecasting targets. We report prospective influenza-like-illness forecasts made for the 2013–2014 U.S. influenza season, and compare the framework’s cross-validated prediction error on historical data to that of a variety of simpler baseline predictors.     

A Novel Tool Improves Existing Estimates of Recent Tuberculosis Transmission in Settings of Sparse Data Collection

In any setting, a proportion of incident active tuberculosis (TB) reflects recent transmission (“recent transmission proportion”), whereas the remainder represents reactivation. Appropriately estimating the recent transmission proportion has important implications for local TB control, but existing approaches have known biases, especially where data are incomplete. We constructed a stochastic individual-based model of a TB epidemic and designed a set of simulations (derivation set) to develop two regression-based tools for estimating the recent transmission proportion from five inputs: underlying TB incidence, sampling coverage, study duration, clustered proportion of observed cases, and proportion of observed clusters in the sample. We tested these tools on a set of unrelated simulations (validation set), and compared their performance against that of the traditional ‘n-1’ approach. In the validation set, the regression tools reduced the absolute estimation bias (difference between estimated and true recent transmission proportion) in the ‘n-1’ technique by a median [interquartile range] of 60% [9%, 82%] and 69% [30%, 87%]. The bias in the ‘n-1’ model was highly sensitive to underlying levels of study coverage and duration, and substantially underestimated the recent transmission proportion in settings of incomplete data coverage. By contrast, the regression models’ performance was more consistent across different epidemiological settings and study characteristics. We provide one of these regression models as a user-friendly, web-based tool. Novel tools can improve our ability to estimate the recent TB transmission proportion from data that are observable (or estimable) by public health practitioners with limited available molecular data.     

Associations between Meteorological Parameters and Influenza Activity in Berlin (Germany), Ljubljana (Slovenia), Castile and León (Spain) and Israeli Districts

Background

Studies in the literature have indicated that the timing of seasonal influenza epidemic varies across latitude, suggesting the involvement of meteorological and environmental conditions in the transmission of influenza. In this study, we investigated the link between meteorological parameters and influenza activity in 9 sub-national areas with temperate and subtropical climates: Berlin (Germany), Ljubljana (Slovenia), Castile and León (Spain) and all 6 districts in Israel.

Methods

We estimated weekly influenza-associated influenza-like-illness (ILI) or Acute Respiratory Infection (ARI) incidence to represent influenza activity using data from each country’s sentinel surveillance during 2000–2011 (Spain) and 2006–2011 (all others). Meteorological data was obtained from ground stations, satellite and assimilated data. Two generalized additive models (GAM) were developed, with one using specific humidity as a covariate and another using minimum temperature. Precipitation and solar radiation were included as additional covariates in both models. The models were adjusted for previous weeks’ influenza activity, and were trained separately for each study location.

Results

Influenza activity was inversely associated (p<0.05) with specific humidity in all locations. Minimum temperature was inversely associated with influenza in all 3 temperate locations, but not in all subtropical locations. Inverse associations between influenza and solar radiation were found in most locations. Associations with precipitation were location-dependent and inconclusive. We used the models to estimate influenza activity a week ahead for the 2010/2011 period which was not used in training the models. With exception of Ljubljana and Israel’s Haifa District, the models could closely follow the observed data especially during the start and the end of epidemic period. In these locations, correlation coefficients between the observed and estimated ranged between 0.55 to 0.91and the model-estimated influenza peaks were within 3 weeks from the observations.

Conclusion

Our study demonstrated the significant link between specific humidity and influenza activity across temperate and subtropical climates, and that inclusion of meteorological parameters in the surveillance system may further our understanding of influenza transmission patterns.     

High Levels of Heterogeneity in the HIV Cascade of Care across Different Population Subgroups in British Columbia,Canada

Background

The HIV cascade of care (cascade) is a comprehensive tool which identifies attrition along the HIV care continuum. We executed analyses to explicate heterogeneity in the cascade across key strata, as well as identify predictors of attrition across stages of the cascade.

Methods

Using linked individual-level data for the population of HIV-positive individuals in BC, we considered the 2011 calendar year, including individuals diagnosed at least 6 months prior, and excluding individuals that died or were lost to follow-up before January 1^st, 2011. We defined five stages in the cascade framework: HIV ‘diagnosed’, ‘linked’ to care, ‘retained’ in care, ‘on HAART’ and virologically ‘suppressed’. We stratified the cascade by sex, age, risk category, and regional health authority. Finally, multiple logistic regression models were built to predict attrition across each stage of the cascade, adjusting for stratification variables.

Results

We identified 7621 HIV diagnosed individuals during the study period; 80% were male and 5% were <30, 17% 30–39, 37% 40–49 and 40% were ≥50 years. Of these, 32% were MSM, 28% IDU, 8% MSM/IDU, 12% heterosexual, and 20% other. Overall, 85% of individuals ‘on HAART’ were ‘suppressed’; however, this proportion ranged from 60%–93% in our various stratifications. Most individuals, in all subgroups, were lost between the stages: ‘linked’ to ‘retained’ and ‘on HAART’ to ‘suppressed’. Subgroups with the highest attrition between these stages included females and individuals <30 years (regardless of transmission risk group). IDUs experienced the greatest attrition of all subgroups. Logistic regression results found extensive statistically significant heterogeneity in attrition across the cascade between subgroups and regional health authorities.

Conclusions

We found that extensive heterogeneity in attrition existed across subgroups and regional health authorities along the HIV cascade of care in B.C., Canada. Our results provide critical information to optimize engagement in care and health service delivery.     

Intrinsic and realized generation intervals in infectious-disease transmission

The generation interval is the interval between the time when an individual is infected by an infector and the time when this infector was infected. Its distribution underpins estimates of the reproductive number and hence informs public health strategies. Empirical generation-interval distributions are often derived from contact-tracing data. But linking observed generation intervals to the underlying generation interval required for modelling purposes is surprisingly not straightforward, and misspecifications can lead to incorrect estimates of the reproductive number, with the potential to misguide interventions to stop or slow an epidemic. Here, we clarify the theoretical framework for three conceptually different generation-interval distributions: the ‘intrinsic’ one typically used in mathematical models and the ‘forward’ and ‘backward’ ones typically observed from contact-tracing data, looking, respectively, forward or backward in time. We explain how the relationship between these distributions changes as an epidemic progresses and discuss how empirical generation-interval data can be used to correctly inform mathematical models.     

Observing others give & take: A computational account of bystanders’ feelings and actions

Social interactions influence people’s feelings and behavior. Here, we propose that a person’s well-being is influenced not only by interactions they experience themselves, but also by those they observe. In particular, we test and quantify the influence of observed selfishness and observed inequality on a bystanders’ feelings and non-costly punishment decisions. We developed computational models that relate others’ (un)selfish acts to observers’ emotional reactions and punishment decisions. These characterize the rules by which others’ interactions are transformed into bystanders’ reactions, and successfully predict those reactions in out-of-sample participants. The models highlight the impact of two social values—‘selfishness aversion’ and ‘inequality aversion’. As for the latter we find that even small violations from perfect equality have a disproportionately large impact on feelings and punishment. In this age of internet and social media we constantly observe others’ online interactions, in addition to in-person interactions. Quantifying the consequences of such observations is important for predicting their impact on society.     

Explaining Bacterial Dispersion on Leaf Surfaces with an Individual-Based Model (PHYLLOSIM)

We developed the individual-based model PHYLLOSIM to explain observed variation in the size of bacterial clusters on plant leaf surfaces (the phyllosphere). Specifically, we tested how different ‘waterscapes’ impacted the diffusion of nutrients from the leaf interior to the surface and the growth of individual bacteria on these nutrients. In the ‘null’ model or more complex ‘patchy’ models, the surface was covered with a continuous water film or with water drops of equal or different volumes, respectively. While these models predicted the growth of individual bacterial immigrants into clusters of variable sizes, they were unable to reproduce experimentally derived, previously published patterns of dispersion which were characterized by a much larger variation in cluster sizes and a disproportionate occurrence of clusters consisting of only one or two bacteria. The fit of model predictions to experimental data was about equally poor (<5%) regardless of whether the water films were continuous or patchy. Only by allowing individual bacteria to detach from developing clusters and re-attach elsewhere to start a new cluster, did PHYLLOSIM come much closer to reproducing experimental observations. The goodness of fit including detachment increased to about 70–80% for all waterscapes. Predictions of this ‘detachment’ model were further supported by the visualization and quantification of bacterial detachment and attachment events at an agarose-water interface. Thus, both model and experiment suggest that detachment of bacterial cells from clusters is an important mechanism underlying bacterial exploration of the phyllosphere.     

The antigenic evolution of influenza: drift or thrift?

It is commonly assumed that antibody responses against the influenza virus are polarized in the following manner: strong antibody responses are directed at highly variable antigenic epitopes, which consequently undergo ‘antigenic drift’, while weak antibody responses develop against conserved epitopes. As the highly variable epitopes are in a constant state of flux, current antibody-based vaccine strategies are focused on the conserved epitopes in the expectation that they will provide some level of clinical protection after appropriate boosting. Here, we use a theoretical model to suggest the existence of epitopes of low variability, which elicit a high degree of both clinical and transmission-blocking immunity. We show that several epidemiological features of influenza and its serological and molecular profiles are consistent with this model of ‘antigenic thrift’, and that identifying the protective epitopes of low variability predicted by this model could offer a more viable alternative to regularly update the influenza vaccine than exploiting responses to weakly immunogenic conserved regions.     

Are V1 Simple Cells Optimized for Visual Occlusions? A Comparative Study

Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex.     

Ethnic inequalities in COVID-19 vaccine uptake and comparison to seasonal influenza vaccine uptake in Greater Manchester,UK: A cohort study

BackgroundCOVID-19 vaccine uptake is lower amongst most minority ethnic groups compared to the White British group in England, despite higher COVID-19 mortality rates. Here, we add to existing evidence by estimating inequalities for 16 minority ethnic groups, examining ethnic inequalities within population subgroups, and comparing the magnitudes of ethnic inequalities in COVID-19 vaccine uptake to those for routine seasonal influenza vaccine uptake.Methods and findingsWe conducted a retrospective cohort study using the Greater Manchester Care Record, which contains de-identified electronic health record data for the population of Greater Manchester, England. We used Cox proportional hazards models to estimate ethnic inequalities in time to COVID-19 vaccination amongst people eligible for vaccination on health or age (50+ years) criteria between 1 December 2020 and 18 April 2021 (138 days of follow-up). We included vaccination with any approved COVID-19 vaccine, and analysed first-dose vaccination only. We compared inequalities between COVID-19 and influenza vaccine uptake adjusting by age group and clinical risk, and used subgroup analysis to identify populations where inequalities were widest. The majority of individuals (871,231; 79.24%) were White British. The largest minority ethnic groups were Pakistani (50,268; 4.75%), ‘other White background’ (43,195; 3.93%), ‘other ethnic group’ (34,568; 3.14%), and Black African (18,802; 1.71%). In total, 83.64% (919,636/1,099,503) of eligible individuals received a COVID-19 vaccine. Uptake was lower compared to the White British group for 15 of 16 minority ethnic groups, with particularly wide inequalities amongst the groups ‘other Black background’ (hazard ratio [HR] 0.42, 95% CI 0.40 to 0.44), Black African (HR 0.43, 95% CI 0.42 to 0.44), Arab (HR 0.43, 95% CI 0.40 to 0.48), and Black Caribbean (HR 0.43, 95% CI 0.42 to 0.45). In total, 55.71% (419,314/752,715) of eligible individuals took up influenza vaccination. Compared to the White British group, inequalities in influenza vaccine uptake were widest amongst the groups ‘White and Black Caribbean’ (HR 0.63, 95% CI 0.58 to 0.68) and ‘White and Black African’ (HR 0.67, 95% CI 0.63 to 0.72). In contrast, uptake was slightly higher than the White British group amongst the groups ‘other ethnic group’ (HR 1.11, 95% CI 1.09 to 1.12) and Bangladeshi (HR 1.08, 95% CI 1.05 to 1.11). Overall, ethnic inequalities in vaccine uptake were wider for COVID-19 than influenza vaccination for 15 of 16 minority ethnic groups. COVID-19 vaccine uptake inequalities also existed amongst individuals who previously took up influenza vaccination. Ethnic inequalities in COVID-19 vaccine uptake were concentrated amongst older and extremely clinically vulnerable adults, and the most income-deprived. A limitation of this study is the focus on uptake of the first dose of COVID-19 vaccination, rather than full COVID-19 vaccination.ConclusionsEthnic inequalities in COVID-19 vaccine uptake exceeded those for influenza vaccine uptake, existed amongst those recently vaccinated against influenza, and were widest amongst those with greatest COVID-19 risk. This suggests the COVID-19 vaccination programme has created additional and different inequalities beyond pre-existing health inequalities. We suggest that further research and policy action is needed to understand and remove barriers to vaccine uptake, and to build trust and confidence amongst minority ethnic communities.

Ruth Elizabeth Watkinson and colleagues estimate inequalities in Covid-19 vaccine uptake for 16 minority ethnic groups and compare them to those in routine seasonal Influenza vaccine uptake.     

Consequences of variable larval dispersal pathways and resulting phenotypic mixtures to the dynamics of marine metapopulations

Larval dispersal can connect distant subpopulations, with important implications for marine population dynamics and persistence, biodiversity conservation and fisheries management. However, different dispersal pathways may affect the final phenotypes, and thus the performance and fitness of individuals that settle into subpopulations. Using otolith microchemical signatures that are indicative of ‘dispersive’ larvae (oceanic signatures) and ‘non-dispersive’ larvae (coastal signatures), we explore the population-level consequences of dispersal-induced variability in phenotypic mixtures for the common triplefin (a small reef fish). We evaluate lipid concentration and otolith microstructure and find that ‘non-dispersive’ larvae (i) have greater and less variable lipid reserves at settlement (and this variability attenuates at a slower rate), (ii) grow faster after settlement, and (iii) experience similar carry-over benefits of lipid reserves on post-settlement growth relative to ‘dispersive’ larvae. We then explore the consequences of phenotypic mixtures in a metapopulation model with two identical subpopulations replenished by variable contributions of ‘dispersive’ and ‘non-dispersive’ larvae and find that the resulting phenotypic mixtures can have profound effects on the size of the metapopulation. We show that, depending upon the patterns of connectivity, phenotypic mixtures can lead to larger metapopulations, suggesting dispersal-induced demographic heterogeneity may facilitate metapopulation persistence.     

A Novel Quantitative Approach to Women’s Reproductive Strategies

The patterned way in which individuals allocate finite resources to various components of reproduction (e.g. mating effort, reproductive timing and parental investment) is described as a reproductive strategy. As energy is limited, trade-offs between and within aspects of reproductive strategies are expected. The first aim of this study was to derive aspects of reproductive strategies using complete reproductive histories from 718 parous Western Australian women. Factor analysis using a subset of these participants resulted in six factors that represented ‘short-term mating strategy’, ‘early onset of sexual activity’, ‘reproductive output’, ‘timing of childbearing’, ‘breastfeeding’, and ‘child spacing’. This factor structure was internally validated by replication using a second independent subset of the data. The second aim of this study examined trade-offs between aspects of reproductive strategies derived from aim one. Factor scores calculated for each woman were incorporated in generalised linear models and interaction terms were employed to examine the effect of mating behaviour on the relationships between reproductive timing, parental investment and overall reproductive success. Early sexual activity correlates with early reproductive onset for women displaying more long-term mating strategies. Women with more short-term mating strategies exhibit a trade-off between child quantity and child quality not observed in women with a long-term mating strategy. However, women with a short-term mating strategy who delay reproductive timing exhibit levels of parental investment (measured as breastfeeding duration per child) similar to that of women with long-term mating strategies. Reproductive delay has fitness costs (fewer births) for women displaying more short-term mating strategies. We provide empirical evidence that reproductive histories of contemporary women reflect aspects of reproductive strategies, and associations between these strategic elements, as predicted from life history theory.     

Predicting Recovery of Voluntary Upper Extremity Movement in Subacute Stroke Patients with Severe Upper Extremity Paresis

DesignProspective cohort study.Methods140 (out of 590) stroke patients with severe UE paresis completed all assessments. Voluntary UE movement was assessed using the UE subscale of the Stroke Rehabilitation Assessment of Movement (STREAM-UE). Two outcome measures, STREAM-UE scores at discharge (DC_STREAM-UE) and changes between admission and discharge (Δ_STREAM-UE), were investigated to represent the final states and improvement of the recovery of voluntary UE movement. Stepwise regression analyses were used to investigate 19 clinical variables and to find the best predictive models of the two outcome measures.ResultsThe participants showed wide variation in both DC_STREAM-UE and Δ_STREAM-UE. 3.6% of the participants almost fully recovered at discharge (DC_STREAM-UE > 15). A large improvement (Δ_STREAM-UE >= 10) occurred in 16.4% of the participants, while 32.9% of the participants did not have any improvement. The four predictors for the DC_STREAM-UE (R² = 35.0%) were ‘baseline STREAM-UE score’, ‘hemorrhagic stroke’, ‘baseline National Institutes of Health Stroke Scale (NIHSS) score’, and ‘cortical lesion excluding primary motor cortex’. The three predictors for the Δ_STREAM-UE (R² = 22.0%) were ‘hemorrhagic stroke’, ‘baseline NIHSS score’, and ‘cortical lesion excluding primary motor cortex’.ConclusionsRecovery of voluntary UE movement varied widely in patients with severe UE paresis after stroke. The predictive power of clinical variables was poor. Both results indicate the complex nature of voluntary UE movement recovery in patients with severe UE paresis after stroke.     

Feedback on Trait or Action Impacts on Caudate and Paracingulum Activity

There is a general conception that positive associations to one’s trait, e.g. ‘I’m clever’, are beneficial for cognitive performance. Scientific evidence shows that this is a simplification. In this functional magnetic resonance imaging (fMRI) study we used written trial-based trait feedback ‘you are clever’, or task feedback ‘your choice was correct’, on each correct response of a rule-switching task, to investigate how the character of positive self-associations influences performance outcome. Twenty participants took part in this crossover design study. We found that trait feedback was less beneficial for motivation and performance improvement, and resulting in enhanced neural activation on more difficult bivalent rule trials. This indicates that the task was treated as more complex in this condition. For example, ‘you are clever’ feedback led to enhanced activation in anterior caudate nucleus, an area known to process uncertainty. We further observed that activation in anterior paracingulate cortex was sensitive to whether self-reflection was imposed by external feedback or generated from internal processes, where the latter activation correlated positively with performance when following after task feedback. Our results illustrate how feedback can evoke self-reflections that either help or hinder motivation and performance, most likely by impacting on processes of uncertainty. The results support social psychological models stipulating that trait focus take resources away from task focus.     

From simple to detailed models for cell polarization

Many mathematical models have been proposed for the process of cell polarization. Some of these are ‘functional models’ that capture a class of dynamical behaviour, whereas others are derived from features of signalling molecules. Some mechanistic models are detailed, and therefore complex, whereas others are simplified. Each type contributes to our understanding of cell polarization. However, the huge variety at different levels of detail makes comparisons challenging. Here, we provide examples of both elementary and more detailed models for polarization. We also display how a recent mathematical method, local perturbation analysis, can provide an appropriate tool for such comparisons. This technique simplifies and speeds up the model development process by revealing the effect of model extensions, parameter variations and in silico manipulations such as knock-out or over-expression of key molecules. Finally, simulations in both one dimension and two dimensions, and particularly in deforming two-dimensional ‘cells’, can highlight behaviour not captured by traditional simulation methods.     

The Influence of Between-Farm Distance and Farm Size on the Spread of Classical Swine Fever during the 1997–1998 Epidemic in The Netherlands

As the size of livestock farms in The Netherlands is on the increase for economic reasons, an important question is how disease introduction risks and risks of onward transmission scale with farm size (i.e. with the number of animals on the farm). Here we use the epidemic data of the 1997–1998 epidemic of Classical Swine Fever (CSF) Virus in The Netherlands to address this question for CSF risks. This dataset is one of the most powerful ones statistically as in this epidemic a total of 428 pig farms where infected, with the majority of farm sizes ranging between 27 and 1750 pigs, including piglets. We have extended the earlier models for the transmission risk as a function of between-farm distance, by adding two factors. These factors describe the effect of farm size on the susceptibility of a ‘receiving’ farm and on the infectivity of a ‘sending’ farm (or ‘source’ farm), respectively. Using the best-fitting model, we show that the size of a farm has a significant influence on both farm-level susceptibility and infectivity for CSF. Although larger farms are both more susceptible to CSF and, when infected, more infectious to other farms than smaller farms, the increase is less than linear. The higher the farm size, the smaller the effect of increments of farm size on the susceptibility and infectivity of a farm. Because of changes in the Dutch pig farming characteristics, a straightforward extrapolation of the observed farm size dependencies from 1997/1998 to present times would not be justified. However, based on our results one may expect that also for the current pig farming characteristics in The Netherlands, farm susceptibility and infectivity depend non-linearly on farm size, with some saturation effect for relatively large farm sizes.     

Network Models of Frequency Modulated Sweep Detection

Frequency modulated (FM) sweeps are common in species-specific vocalizations, including human speech. Auditory neurons selective for the direction and rate of frequency change in FM sweeps are present across species, but the synaptic mechanisms underlying such selectivity are only beginning to be understood. Even less is known about mechanisms of experience-dependent changes in FM sweep selectivity. We present three network models of synaptic mechanisms of FM sweep direction and rate selectivity that explains experimental data: (1) The ‘facilitation’ model contains frequency selective cells operating as coincidence detectors, summing up multiple excitatory inputs with different time delays. (2) The ‘duration tuned’ model depends on interactions between delayed excitation and early inhibition. The strength of delayed excitation determines the preferred duration. Inhibitory rebound can reinforce the delayed excitation. (3) The ‘inhibitory sideband’ model uses frequency selective inputs to a network of excitatory and inhibitory cells. The strength and asymmetry of these connections results in neurons responsive to sweeps in a single direction of sufficient sweep rate. Variations of these properties, can explain the diversity of rate-dependent direction selectivity seen across species. We show that the inhibitory sideband model can be trained using spike timing dependent plasticity (STDP) to develop direction selectivity from a non-selective network. These models provide a means to compare the proposed synaptic and spectrotemporal mechanisms of FM sweep processing and can be utilized to explore cellular mechanisms underlying experience- or training-dependent changes in spectrotemporal processing across animal models. Given the analogy between FM sweeps and visual motion, these models can serve a broader function in studying stimulus movement across sensory epithelia.     

A Novel Video Tracking Method to Evaluate the Effect of Influenza Infection and Antiviral Treatment on Ferret Activity

Ferrets are the preferred animal model to assess influenza virus infection, virulence and transmission as they display similar clinical symptoms and pathogenesis to those of humans. Measures of disease severity in the ferret include weight loss, temperature rise, sneezing, viral shedding and reduced activity. To date, the only available method for activity measurement has been the assignment of an arbitrary score by a ‘blind’ observer based on pre-defined responsiveness scale. This manual scoring method is subjective and can be prone to bias. In this study, we described a novel video-tracking methodology for determining activity changes in a ferret model of influenza infection. This method eliminates the various limitations of manual scoring, which include the need for a sole ‘blind’ observer and the requirement to recognise the ‘normal’ activity of ferrets in order to assign relative activity scores. In ferrets infected with an A(H1N1)pdm09 virus, video-tracking was more sensitive than manual scoring in detecting ferret activity changes. Using this video-tracking method, oseltamivir treatment was found to ameliorate the effect of influenza infection on activity in ferret. Oseltamivir treatment of animals was associated with an improvement in clinical symptoms, including reduced inflammatory responses in the upper respiratory tract, lower body weight loss and a smaller rise in body temperature, despite there being no significant reduction in viral shedding. In summary, this novel video-tracking is an easy-to-use, objective and sensitive methodology for measuring ferret activity.     

Predicting demographically sustainable rates of adaptation: can great tit breeding time keep pace with climate change?

Populations need to adapt to sustained climate change, which requires micro-evolutionary change in the long term. A key question is how the rate of this micro-evolutionary change compares with the rate of environmental change, given that theoretically there is a ‘critical rate of environmental change’ beyond which increased maladaptation leads to population extinction. Here, we parametrize two closely related models to predict this critical rate using data from a long-term study of great tits (Parus major). We used stochastic dynamic programming to predict changes in optimal breeding time under three different climate scenarios. Using these results we parametrized two theoretical models to predict critical rates. Results from both models agreed qualitatively in that even ‘mild’ rates of climate change would be close to these critical rates with respect to great tit breeding time, while for scenarios close to the upper limit of IPCC climate projections the calculated critical rates would be clearly exceeded with possible consequences for population persistence. We therefore tentatively conclude that micro-evolution, together with plasticity, would rescue only the population from mild rates of climate change, although the models make many simplifying assumptions that remain to be tested.     

Efficient and robust coding in heterogeneous recurrent networks

Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumptions: 1) every spike is decoded linearly and 2) the network aims to reduce the mean-squared error between the input and the estimate. From this we derive a class of predictive coding networks, that unifies encoding and decoding and in which we can investigate the difference between homogeneous networks and heterogeneous networks, in which each neurons represents different features and has different spike-generating properties. We find that in this framework, ‘type 1’ and ‘type 2’ neurons arise naturally and networks consisting of a heterogeneous population of different neuron types are both more efficient and more robust against correlated noise. We make two experimental predictions: 1) we predict that integrators show strong correlations with other integrators and resonators are correlated with resonators, whereas the correlations are much weaker between neurons with different coding properties and 2) that ‘type 2’ neurons are more coherent with the overall network activity than ‘type 1’ neurons.