Estimating the impact of the COVID-19 pandemic on dengue in Brazil

Atypical dengue prevalence was observed in 2020 in many dengue-endemic countries, including Brazil. Evidence suggests that the pandemic disrupted not only dengue dynamics due to changes in mobility patterns, but also several aspects of dengue surveillance, such as care seeking behavior, care availability, and monitoring systems. However, we lack a clear understanding of the overall impact on dengue in different parts of the country as well as the role of individual causal drivers. In this study, we estimated the gap between expected and observed dengue cases in 2020 using an interrupted time series design with forecasts from a neural network and a structural Bayesian time series model. We also decomposed the gap into the impacts of climate conditions, pandemic-induced changes in reporting, human susceptibility, and human mobility. We find that there is considerable variation across the country in both overall pandemic impact on dengue and the relative importance of individual drivers. Increased understanding of the causal mechanisms driving the 2020 dengue season helps mitigate some of the data gaps caused by the COVID-19 pandemic and is critical to developing effective public health interventions to control dengue in the future.

Machine-Learning-Based Forecasting of Dengue Fever in Brazilian Cities Using Epidemiologic and Meteorological Variables

Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we developed a model for predicting monthly dengue cases in Brazilian cities 1 month ahead, using data from 2007-2019. We compared different machine learning algorithms and feature selection methods using epidemiologic and meteorological variables. We found that different models worked best in different cities, and a random forests model trained on monthly dengue cases performed best overall. It produced lower errors than a seasonal naive baseline model, gradient boosting regression, a feed-forward neural network, or support vector regression. For each city, we computed the mean absolute error between predictions and true monthly numbers of dengue cases on the test data set. The median error across all cities was 12.2 cases. This error was reduced to 11.9 when selecting the optimal combination of algorithm and input features for each city individually. Machine learning and especially decision tree ensemble models may contribute to dengue surveillance in Brazil, as they produce low out-of-sample prediction errors for a geographically diverse set of cities.

Forecasting new diseases in low-data settings using transfer learning

Recent infectious disease outbreaks, such as the COVID-19 pandemic and the Zika epidemic in Brazil, have demonstrated both the importance and difficulty of accurately forecasting novel infectious diseases. When new diseases first emerge, we have little knowledge of the transmission process, the level and duration of immunity to reinfection, or other parameters required to build realistic epidemiological models. Time series forecasts and machine learning, while less reliant on assumptions about the disease, require large amounts of data that are also not available in early stages of an outbreak. In this study, we examine how knowledge of related diseases can help make predictions of new diseases in data-scarce environments using transfer learning. We implement both an empirical and a synthetic approach. Using data from Brazil, we compare how well different machine learning models transfer knowledge between two different dataset pairs: case counts of (i) dengue and Zika, and (ii) influenza and COVID-19. In the synthetic analysis, we generate data with an SIR model using different transmission and recovery rates, and then compare the effectiveness of different transfer learning methods. We find that transfer learning offers the potential to improve predictions, even beyond a model based on data from the target disease, though the appropriate source disease must be chosen carefully. While imperfect, these models offer an additional input for decision makers for pandemic response.

1218Estimating the causal effect of mobility on Dengue during the COVID-19 pandemic

Background

The COVID-19 pandemic led to a reduction in human mobility which occurred randomly (in time) and is not linked to any other Dengue risk factors. This gives rise to a quasi-experimental situation to assess the impact of mobility reduction on Dengue Fever in Brazilian cities using propensity score matching.

Methods

We match weeks during the peak pandemic period for 37 cities in São Paulo state with comparable prior periods based on instruments for mosquito population size and human susceptibility. By matching within cities, we also control for city-level characteristics, such as landscape or population density. We compute propensity scores using logistic regression and Random Forests and implement both one-to-one and one-to-many matching with calipers.

Results

We compare the Sample Average Treatment Effect on the Treated (SATT) across models and find variation in the direction of the causal effect. In 12 cities, mobility reductions are linked to more Dengue cases, while fewer cases are reported in 9 cities. The remaining cities are sensitive to the model chosen.

Conclusions

The SATT of mobility on Dengue varies across the cities in our sample, with more cities experiencing an increase in cases during the COVID-19 pandemic.

Key messages

A quasi-experimental analysis suggests that there is a a causal effect of mobility on Dengue that varies across cities in São Paulo state.

1222Estimating the causal effect of mobility on Dengue during the COVID-19 pandemic

Background

Research suggests that human mobility is associated with the spread of Dengue Fever [1]. The gold-standard in estimating a causal effect are randomized experiments. As these are neither feasible nor ethical in estimating mobility's impact on Dengue, we rely on methods that make use of observational data. During the COVID-19 pandemic, most of the world saw a sudden drop in long- and short-distance travel. In March 2020, inner- and inter-city transit dropped severely and uniformly across cities in Brazil. The drop was random (in time) and is not linked to any of the other factors that affect Dengue, such as the mosquito population size. This gives rise to a quasi-experimental situation to assess the impact of mobility reduction on Dengue Fever in Brazilian cities.

The potential impact of the COVID-19 pandemic on Dengue Fever has been recognized globally [2, 3] and first attempts at estimating this impact have been published. Conceição et al. (2021) [4] find an association between social isolation and Dengue in the Brazilian state of São Paulo using a negative binomial regression model. Risk of Dengue infection in São Paulo decreases by 9% twenty days after isolation, defined as mobility reduction of 1-30% relative to baseline mobility prior to the pandemic. Lim et al. (2020) [5] use a Regression Discontinuity Design (RDD) to estimate the causal effect of social distancing policies on Dengue at the state-level in three South-East Asian countries. Results for Thailand indicate that social distancing raises Dengue incidence by 0.431 cases per 10,000 people. The authors explain this effect by referring to the increased time spent at home, where the risk of infection is higher than in other locations, such as workplaces. No significant effect was found for Singapore and Malaysia.

Figure 1 shows the time series of Dengue cases in cities in São Paulo over time. The top panel compares the evolution during the pandemic year 2020 with the average of prior distributions. We note that early 2020 saw greater incidence and an earlier peak than in prior years. This could be due to the stark mobility reduction that occurred in March (epidemiological weeks 11-13). However, the evolution of Dengue cases from 2020 is not unprecedented, as can be seen in the lower panel. Here we compare the time series in individual years and note that 2020 had a similar distribution as the year 2016. The peak in these years occurred earlier than in the years 2015 and 2019. In the following analysis, we aim to better understand the possible causal impact of mobility reduction on Dengue.

Methods

We implement propensity score matching to assess the causal effect of mobility on Dengue during the COVID-19 pandemic in São Paulo state in Brazil. We match weeks during the peak pandemic period (March-June 2020) to comparable previous periods based instruments for the mosquito population size and human susceptibility to Dengue. The breeding conditions and thus mosquito population size is approximated using climate factors. We estimate the level of susceptibility within the human population by taking the average number of infections in the same month of the past three years, so as to approximate the duration of partial immunity after Dengue infection. By matching within a given city, we also control for city-level characteristics that may affect Dengue, such as landscape and environment factors, socio-economic situation, or population density.

We use weekly data on Dengue cases and climate (rainfall, temperature, humidity) in 37 cities in the state of São Paulo, Brazil, from 2015 until 2020. The peak isolation period (March - June 2020) was determined using monthly traffic volume at toll stations, where isolation is the period in which passenger traffic was consistently below the pre-pandemic (2011-2019) minimum. We also leverage the regional COVID-19 Community Mobility Reports published by Google to assess the change in mobility due to the pandemic.

We compute the propensity score using both a logistic regression model and a 100-tree Random Forest model with five-fold cross-validation. In both cases, we observe good overlap in the propensity scores among treated and control groups, suggesting that conditions for matching are met (see figure 2).

We implement both one-to-one and one-to-many matching with calipers. After matching, we observe similar distributions of the control variables among the treated and matched control periods. Figure 3 illustrates this using the distributions of the one-to-one matching based on propensity scores of the logistic regression models.

Results

We compare the Sample Average Treatment Effect on the Treated (SATT) across the four models and find variation in the direction of the causal effect. In 12 cities, mobility reductions are linked to more Dengue cases with results being robust to the propensity score estimation method and matching type. Fewer Dengue cases are reported in 9 cities during the pandemic, regardless of which model is chosen. The remaining cities are sensitive to the model chosen: in 6 cases, three of the four models produced a positive effect, while the majority indicated lower Dengue incidence in 5 cities.

The diversity of results may be attributed to differing travel patterns across cities. Long-distance travel hubs like São Paulo (the state capital) and São Carlos (university city with flux of students across the country) demonstrate a negative relationship of a SATT of -18.0 and -18.8 cases per week, respectively. Cities that experience mostly local travel demonstrate the converse effect. This role of different mobility types should be explored further.

Additional robustness checks should be performed to understand the role of additional control variables or varying time series length.

Conclusions

The SATT of mobility on Dengue varies across the cities in our sample, with more cities experiencing an increase in cases during the COVID-19 pandemic.

Key messages

The travel reduction due to the COVID-19 pandemic enables a quasi-experimental analysis of mobility on Dengue. Our results suggest that there is a a causal effect of mobility on Dengue that varies across cities in São Paulo state. Specific characteristics of cities may help explain where mobility leads to Dengue spreading and where home-based infections are the primary disease drivers.

Prediction models and questionnaires developed to predict vitamin D status in adults: a systematic review

A systematic review of prediction models/questionnaires developed to identify people with deficient/insufficient vitamin D status shows the potential of self-reported information to estimate vitamin D status. The objective is to identify and compare existing screening tools, developed to identify vitamin D deficiency or insufficiency in adults. A systematic search of literature was conducted using MEDLINE, Scopus, Web of Science and CINAHL databases. Risk of bias and applicability concerns were assessed by quality assessment of diagnostic accuracy studies (QUADAS-2). Data were extracted on socio-demographic, anthropometric, risk factors, serum 25 hydroxyvitamin D [25(OH)D] levels, statistical methods and predictive ability. A total of 12 studies were considered for inclusion for this systematic review after screening of 4851 abstracts and 15 full-text articles. Ten of twelve studies developed prediction models and 2 studies developed questionnaires. The majority of studies had low risk of bias and applicability as assessed by QUADAS-2. All studies included only self-reported predictors of vitamin D status in their final models and development of scores. Sunlight exposure and related factors were important significant contributors to the predictive ability of the models and/or questionnaires. Sensitivity and specificity of the prediction models or questionnaires ranged from 55 to 91% and 35 to 84%, respectively. Six out of twelve studies converted final models to scores associated with vitamin D status. There was no evidence that any of these existing tools have been translated into clinical practice. The prediction models or questionnaires identified in this systematic review were moderately sensitive and specific for identifying people with vitamin D deficiency or insufficiency. The substantial contribution of sunlight exposure to the prediction of vitamin D status highlights the importance of including this information when developing vitamin D screening tools.

Narrative structure of A Song of Ice and Fire creates a fictional world with realistic measures of social complexity

Network science and data analytics are used to quantify static and dynamic structures in George R. R. Martin's epic novels, A Song of Ice and Fire, works noted for their scale and complexity. By tracking the network of character interactions as the story unfolds, it is found that structural properties remain approximately stable and comparable to real-world social networks. Furthermore, the degrees of the most connected characters reflect a cognitive limit on the number of concurrent social connections that humans tend to maintain. We also analyze the distribution of time intervals between significant deaths measured with respect to the in-story timeline. These are consistent with power-law distributions commonly found in interevent times for a range of nonviolent human activities in the real world. We propose that structural features in the narrative that are reflected in our actual social world help readers to follow and to relate to the story, despite its sprawling extent. It is also found that the distribution of intervals between significant deaths in chapters is different to that for the in-story timeline; it is geometric rather than power law. Geometric distributions are memoryless in that the time since the last death does not inform as to the time to the next. This provides measurable support for the widely held view that significant deaths in A Song of Ice and Fire are unpredictable chapter by chapter.

Disease and information spreading at different speeds in multiplex networks

Nowadays, one of the challenges we face when carrying out modeling of epidemic spreading is to develop methods to control disease transmission. In this article we study how the spreading of knowledge of a disease affects the propagation of that disease in a population of interacting individuals. For that, we analyze the interaction between two different processes on multiplex networks: the propagation of an epidemic using the susceptible-infected-susceptible dynamics and the dissemination of information about the disease-and its prevention methods-using the unaware-aware-unaware dynamics, so that informed individuals are less likely to be infected. Unlike previous related models where disease and information spread at the same time scale, we introduce here a parameter that controls the relative speed between the propagation of the two processes. We study the behavior of this model using a mean-field approach that gives results in good agreement with Monte Carlo simulations on homogeneous complex networks. We find that increasing the rate of information dissemination reduces the disease prevalence, as one may expect. However, increasing the speed of the information process as compared to that of the epidemic process has the counterintuitive effect of increasing the disease prevalence. This result opens an interesting discussion about the effects of information spreading on disease propagation.

Role of zero clusters in exchange-driven growth with and without input

The exchange-driven growth model describes the mean-field kinetics of a population of composite particles (clusters) subject to pairwise exchange interactions. Exchange in this context means that upon interaction of two clusters, one loses a constituent unit (monomer) and the other gains this unit. Two variants of the exchange-driven growth model appear in applications. They differ in whether clusters of zero size are considered active or passive. In the active case, clusters of size zero can acquire a monomer from clusters of positive size. In the passive case they cannot, meaning that clusters reaching size zero are effectively removed from the system. We show that the large-time behavior is very different for the two variants of the model. We first consider an isolated system. In the passive case, the cluster size distribution tends towards a self-similar evolution and the typical cluster size grows as a power of time. In the active case, we identify a broad class of kernels for which the the cluster size distribution tends to a nontrivial time-independent equilibrium in which the typical cluster size is finite. We next consider a nonisolated system in which monomers are input at a constant rate. In the passive case, the cluster size distribution again attains a self-similar profile in which the typical cluster size grows as a power of time. In the active case, a surprising new behavior is found: the cluster size distribution asymptotes to the same equilibrium profile found in the isolated case but with an amplitude that increases linearly with time.

Epidemic spreading with awareness and different timescales in multiplex networks

One of the major issues in theoretical modeling of epidemic spreading is the development of methods to control the transmission of an infectious agent. Human behavior plays a fundamental role in the spreading dynamics and can be used to stop a disease from spreading or to reduce its burden, as individuals aware of the presence of a disease can take measures to reduce their exposure to contagion. In this paper, we propose a mathematical model for the spread of diseases with awareness in complex networks. Unlike previous models, the information is propagated following a generalized Maki-Thompson rumor model. Flexibility on the timescale between information and disease spreading is also included. We verify that the velocity characterizing the diffusion of information awareness greatly influences the disease prevalence. We also show that a reduction in the fraction of unaware individuals does not always imply a decrease of the prevalence, as the relative timescale between disease and awareness spreading plays a crucial role in the systems' dynamics. This result is shown to be independent of the network topology. We finally calculate the epidemic threshold of our model, and show that it does not depend on the relative timescale. Our results provide a new view on how information influence disease spreading and can be used for the development of more efficient methods for disease control.

Stationary mass distribution and nonlocality in models of coalescence and shattering

We study the asymptotic properties of the steady state mass distribution for a class of collision kernels in an aggregation-shattering model in the limit of small shattering probabilities. It is shown that the exponents characterizing the large and small mass asymptotic behavior of the mass distribution depend on whether the collision kernel is local (the aggregation mass flux is essentially generated by collisions between particles of similar masses) or nonlocal (collision between particles of widely different masses give the main contribution to the mass flux). We show that the nonlocal regime is further divided into two subregimes corresponding to weak and strong nonlocality. We also observe that at the boundaries between the local and nonlocal regimes, the mass distribution acquires logarithmic corrections to scaling and calculate these corrections. Exact solutions for special kernels and numerical simulations are used to validate some nonrigorous steps used in the analysis. Our results show that for local kernels, the scaling solutions carry a constant flux of mass due to aggregation, whereas for the nonlocal case there is a correction to the constant flux exponent. Our results suggest that for general scale-invariant kernels, the universality classes of mass distributions are labeled by two parameters: the homogeneity degree of the kernel and one further number measuring the degree of the nonlocality of the kernel.

Fitness voter model: Damped oscillations and anomalous consensus

We study the dynamics of opinion formation in a heterogeneous voter model on a complete graph, in which each agent is endowed with an integer fitness parameter k≥0, in addition to its + or - opinion state. The evolution of the distribution of k-values and the opinion dynamics are coupled together, so as to allow the system to dynamically develop heterogeneity and memory in a simple way. When two agents with different opinions interact, their k-values are compared, and with probability p the agent with the lower value adopts the opinion of the one with the higher value, while with probability 1-p the opposite happens. The agent that keeps its opinion (winning agent) increments its k-value by one. We study the dynamics of the system in the entire 0≤p≤1 range and compare with the case p=1/2, in which opinions are decoupled from the k-values and the dynamics is equivalent to that of the standard voter model. When 0≤p<1/2, agents with higher k-values are less persuasive, and the system approaches exponentially fast to the consensus state of the initial majority opinion. The mean consensus time τ appears to grow logarithmically with the number of agents N, and it is greatly decreased relative to the linear behavior τ∼N found in the standard voter model. When 1/2<p≤1, agents with higher k-values are more persuasive, and the system initially relaxes to a state with an even coexistence of opinions, but eventually reaches consensus by finite-size fluctuations. The approach to the coexistence state is monotonic for 1/2<p<p_{o}≃0.8, while for p_{o}≤p≤1 there are damped oscillations around the coexistence value. The final approach to coexistence is approximately a power law t^{-b(p)} in both regimes, where the exponent b increases with p. Also, τ increases respect to the standard voter model, although it still scales linearly with N. The p=1 case is special, with a relaxation to coexistence that scales as t^{-2.73} and a consensus time that scales as τ∼N^{β}, with β≃1.45.

Dynamically controlled deposition of colloidal nanoparticle suspension in evaporating drops using laser radiation

Dynamic control of the distribution of polystyrene suspended nanoparticles in evaporating droplets is investigated using a 2.9 μm high power laser. Under laser radiation a droplet is locally heated and fluid flows are induced that overcome the capillary flow, and thus a reversal of the coffee-stain effect is observed. Suspension particles are accumulated in a localised area, one order of magnitude smaller than the original droplet size. By scanning the laser beam over the droplet, particles can be deposited in an arbitrary pattern. This finding raises the possibility for direct laser writing of suspended particles through a liquid layer. Furthermore, a highly uniform coating is possible by manipulating the laser beam diameter and exposure time. The effect is expected to be universally applicable to aqueous solutions independent of solutes (either particles or molecules) and deposited substrates.

Nonlinear least-squares method for the inverse droplet coagulation problem

If the rates, K(x,y), at which particles of size x coalesce with particles of size y is known, then the mean-field evolution of the particle size distribution of an ensemble of irreversibly coalescing particles is described by the Smoluchowski equation. We study the corresponding inverse problem which aims to determine the coalescence rates K(x,y) from measurements of the particle size distribution. We assume that K(x,y) is a homogeneous function of its arguments, a case which occurs commonly in practice. The problem of determining K(x,y), a function to two variables, then reduces to the simpler problem of determining a function of a single variable plus two exponents, μ and ν, which characterize the scaling properties of K(x,y). The price of this simplification is that the resulting least-squares problem is nonlinear in the exponents μ and ν. We demonstrate the effectiveness of the method on a selection of coalescence problems arising in polymer physics, cloud science, and astrophysics. The applications include examples in which the particle size distribution is stationary owing to the presence of sources and sinks of particles and examples in which the particle size distribution is undergoing self-similar relaxation in time.

Collective oscillations in irreversible coagulation driven by monomer inputs and large-cluster outputs

We describe collective oscillatory behavior in the kinetics of irreversible coagulation with a constant input of monomers and removal of large clusters. For a broad class of collision rates, this system reaches a nonequilibrium stationary state at large times and the cluster size distribution tends to a universal form characterized by a constant flux of mass through the space of cluster sizes. Universality, in this context, means that the stationary state becomes independent of the cutoff as the cutoff grows. This universality is lost, however, if the aggregation rate between large and small clusters increases sufficiently steeply as a function of cluster sizes. We identify a transition to a regime in which the stationary state vanishes as the cutoff grows. This nonuniversal stationary state becomes unstable as the cutoff is increased. It undergoes a Hopf bifurcation after which the stationary state is replaced by persistent and periodic collective oscillations. These oscillations, which bear some similarities to relaxation oscillations in excitable media, carry pulses of mass through the space of cluster sizes such that the average mass flux through any cluster size remains constant. Universality is partially restored in the sense that the scaling of the period and amplitude of oscillation is inherited from the dynamical scaling exponents of the universal regime.

Driven Brownian coagulation of polymers

We present an analysis of the mean-field kinetics of Brownian coagulation of droplets and polymers driven by input of monomers which aims to characterize the long time behavior of the cluster size distribution as a function of the inverse fractal dimension, a, of the aggregates. We find that two types of long time behavior are possible. For 0≤a<1/2 the size distribution reaches a stationary state with a power law distribution of cluster sizes having exponent 3/2. The amplitude of this stationary state is determined exactly as a function of a. For 1/2<a≤1, the cluster size distribution never reaches a stationary state. Instead a bimodal distribution is formed in which a narrow population of small clusters near the monomer scale is separated by a gap (where the cluster size distribution is effectively zero) from a population of large clusters which continue to grow for all time by absorbing small clusters. The marginal case, a=1/2, is difficult to analyze definitively, but we argue that the cluster size distribution becomes stationary and there is a logarithmic correction to the algebraic tail.

Instantaneous gelation in Smoluchowski's coagulation equation revisited

We study the solutions of the Smoluchowski coagulation equation with a regularization term which removes clusters from the system when their mass exceeds a specified cutoff size, M. We focus primarily on collision kernels which would exhibit an instantaneous gelation transition in the absence of any regularization. Numerical simulations demonstrate that for such kernels with monodisperse initial data, the regularized gelation time decreases as M increases, consistent with the expectation that the gelation time is zero in the unregularized system. This decrease appears to be a logarithmically slow function of M, indicating that instantaneously gelling kernels may still be justifiable as physical models despite the fact that they are highly singular in the absence of a cutoff. We also study the case when a source of monomers is introduced in the regularized system. In this case a stationary state is reached. We present a complete analytic description of this regularized stationary state for the model kernel, K(m(1),m(2)) = max{m(1),m(2)}(ν), which gels instantaneously when M → ∞ if ν>1. The stationary cluster size distribution decays as a stretched exponential for small cluster sizes and crosses over to a power law decay with exponent ν for large cluster sizes. The total particle density in the stationary state slowly vanishes as [(ν-1)log M](-1/2) when M → ∞. The approach to the stationary state is nontrivial: Oscillations about the stationary state emerge from the interplay between the monomer injection and the cutoff, M, which decay very slowly when M is large. A quantitative analysis of these oscillations is provided for the addition model which describes the situation in which clusters can only grow by absorbing monomers.

Aggregation-fragmentation processes and decaying three-wave turbulence

We use a formal correspondence between the isotropic three-wave kinetic equation and the rate equations for a nonlinear fragmentation-aggregation process to study the wave frequency power spectrum of decaying three-wave turbulence in the infinite capacity regime. We show that the transient spectral exponent is lambda+1 , where lambda is the degree of homogeneity of the wave interaction kernel and derive a formula for the decay amplitude. When lambda=0 the transient exponent coincides with the thermodynamic equilibrium exponent leading to logarithmic corrections to scaling which we calculate explicitly for the case of constant interaction kernel.

Dynamical scaling and the finite-capacity anomaly in three-wave turbulence

We present a systematic study of the dynamical scaling process leading to the establishment of the Kolmogorov-Zakharov (KZ) spectrum in weak three-wave turbulence. In the finite-capacity case, in which the transient spectrum reaches infinite frequency in finite time, the dynamical scaling exponent is anomalous in the sense that it cannot be determined from dimensional considerations. As a consequence, the transient spectrum preceding the establishment of the steady state is steeper than the KZ spectrum. Constant energy flux is actually established from right to left in frequency space after the singularity of the transient solution. From arguments based on entropy production, a steeper transient spectrum is heuristically plausible.

Probability distribution of power fluctuations in turbulence

We study local power fluctuations in numerical simulations of stationary, homogeneous, isotropic turbulence in two and three dimensions with Gaussian forcing. Due to the near-Gaussianity of the one-point velocity distribution, the probability distribution function (pdf) of the local power is well modeled by the pdf of the product of two joint normally distributed variables. In appropriate units, this distribution is parametrized only by the mean dissipation rate, epsilon. The large deviation function for this distribution is calculated exactly and shown to satisfy a fluctuation relation (FR) with a coefficient which depends on epsilon. This FR is entirely statistical in origin. The deviations from the model pdf are most pronounced for positive fluctuations of the power and can be traced to a slightly faster than Gaussian decay of the tails of the one-point velocity pdf. The resulting deviations from the FR are consistent with several recent experimental studies.

Constant flux relation for diffusion-limited cluster-cluster aggregation

In a nonequilibrium system, a constant flux relation (CFR) expresses the fact that a constant flux of a conserved quantity exactly determines the scaling of the particular correlation function linked to the flux of that conserved quantity. This is true regardless of whether mean-field theory is applicable or not. We focus on cluster-cluster aggregation and discuss the consequences of mass conservation for the steady state of aggregation models with a monomer source in the diffusion-limited regime. We derive the CFR for the flux-carrying correlation function for binary aggregation with a general scale-invariant kernel and show that this exponent is unique. It is independent of both the dimension and of the details of the spatial transport mechanism, a property which is very atypical in the diffusion-limited regime. We then discuss in detail the "locality criterion" which must be satisfied in order for the CFR scaling to be realizable. Locality may be checked explicitly for the mean-field Smoluchowski equation. We show that if it is satisfied at the mean-field level, it remains true over some finite range as one perturbatively decreases the dimension of the system below the critical dimension, d_{c}=2 , entering the fluctuation-dominated regime. We turn to numerical simulations to verify locality for a range of systems in one dimension which are, presumably, beyond the perturbative regime. Finally, we illustrate how the CFR scaling may break down as a result of a violation of locality or as a result of finite size effects and discuss the extent to which the results apply to higher order aggregation processes.

Craig's XY distribution and the statistics of Lagrangian power in two-dimensional turbulence

We examine the probability distribution function (PDF) of the energy injection rate (power) in numerical simulations of stationary two-dimensional (2D) turbulence in the Lagrangian frame. The simulation is designed to mimic an electromagnetically driven fluid layer, a well-documented system for generating 2D turbulence in the laboratory. In our simulations, the forcing and velocity fields are close to Gaussian. On the other hand, the measured PDF of injected power is very sharply peaked at zero, suggestive of a singularity there, with tails which are exponential but asymmetric. Large positive fluctuations are more probable than large negative fluctuations. It is this asymmetry of the tails which leads to a net positive mean value for the energy input despite the most probable value being zero. The main features of the power distribution are well described by Craig's XY distribution for the PDF of the product of two correlated normal variables. We show that the power distribution should exhibit a logarithmic singularity at zero and decay exponentially for large absolute values of the power. We calculate the asymptotic behavior and express the asymmetry of the tails in terms of the correlation coefficient of the force and velocity. We compare the measured PDFs with the theoretical calculations and briefly discuss how the power PDF might change with other forcing mechanisms.

Dynamics of energy condensation in two-dimensional turbulence

We report a numerical study, supplemented by phenomenological explanations, of "energy condensation" in forced 2D turbulence in a biperiodic box. Condensation is a finite size effect which occurs after the standard inverse cascade reaches the size of the system. It leads to the emergence of a coherent vortex dipole. We show that the time growth of the dipole is self-similar, and it contains most of the injected energy, thus resulting in an energy spectrum which is markedly steeper than the standard k{-5/3} one. Once the coherent component is subtracted, however, the remaining fluctuations have a spectrum close to k{-1}. The fluctuations decay slowly as the coherent part grows.

Constant flux relation for driven dissipative systems

Conservation laws constrain the stationary state statistics of driven dissipative systems because the average flux of a conserved quantity between driving and dissipation scales should be constant. This requirement leads to a universal scaling law for flux-measuring correlation functions, which generalizes the 4/5th law of Navier-Stokes turbulence. We demonstrate the utility of this simple idea by deriving new exact scaling relations for models of aggregating particle systems in the fluctuation-dominated regime and for energy and wave action cascades in models of strong wave turbulence.

Condensation of classical nonlinear waves

We study the formation of a large-scale coherent structure (a condensate) in classical wave equations by considering the defocusing nonlinear Schrödinger equation as a representative model. We formulate a thermodynamic description of the classical condensation process by using a wave turbulence theory with ultraviolet cutoff. In three dimensions the equilibrium state undergoes a phase transition for sufficiently low energy density, while no transition occurs in two dimensions, in complete analogy with standard Bose-Einstein condensation in quantum systems. On the basis of a modified wave turbulence theory, we show that the nonlinear interaction makes the transition to condensation subcritical. The theory is in quantitative agreement with the numerical integration of the nonlinear Schrödinger equation.

Breakdown of Kolmogorov scaling in models of cluster aggregation

We describe a model of cluster aggregation with a source which provides a rare example of an analytically tractable turbulent system. The steady state is characterized by a constant mass flux from small masses to large. Thus it can be studied using a phenomenological theory, inspired by Kolmogorov's 1941 theory, which assumes constant flux and self-similarity. We prove that such self-similarity is violated in dimensions less than or equal to two. We then use dynamical renormalization group techniques to show that the scaling of multipoint correlation functions implies nontrivial multifractality. The analytical results are supported by Monte Carlo simulations.

Stationary Kolmogorov solutions of the Smoluchowski aggregation equation with a source term

In this paper we show how the method of Zakharov transformations may be used to analyze the stationary solutions of the Smoluchowski aggregation equation with a source term for arbitrary homogeneous coagulation kernel. The resulting power-law mass distributions are of Kolmogorov type in the sense that they carry a constant flux of mass from small masses to large. They are valid for masses much larger than the characteristic mass of the source. We derive a "locality criterion," expressed in terms of the asymptotic properties of the kernel, that must be satisfied in order for the Kolmogorov spectrum to be an admissible solution. Whether a given kernel leads to a gelation transition or not can be determined by computing the mass capacity of the Kolmogorov spectrum. As an example, we compute the exact stationary state for the family of kernels, K(zeta) ( m(1), m(2) )= ( m(1) m(2) )(zeta/2) which includes both gelling and nongelling cases, reproducing the known solution in the case zeta=0. Surprisingly, the Kolmogorov constant is the same for all kernels in this family.

Warm cascades and anomalous scaling in a diffusion model of turbulence

A phenomenological turbulence model in which the energy spectrum obeys a nonlinear diffusion equation is analyzed. The general steady state contains a nonlinear mixture of the constant-flux Kolmogorov and fluxless thermodynamic components. Such "warm cascade" solutions describe a bottleneck phenomenon of spectrum stagnation near the dissipative scale. Transient self-similar solutions describing a finite-time formation of steady cascades are analyzed and found to exhibit nontrivial scaling behavior.

Discreteness and quasiresonances in weak turbulence of capillary waves

A numerical study is presented which deals with the kinematics of quasiresonant energy transfer in a system of capillary waves with a discrete wave number space in a periodic box. For a given set of initially excited modes and a given level of resonance broadening, the modes of the system are partitioned into two classes, one active, the other forbidden. For very weak nonlinearity the active modes are very sparse. It is possible that this sparsity explains discrepancies between the values of the Kolmogorov constant measured in numerical simulations of weakly turbulent cascades and the theoretical values obtained from the continuum theory. There is a critical level of nonlinearity below which the set of active modes has finite radius in wave number space. In this regime, an energy cascade to dissipative scales may not be possible and the usual Kolmogorov spectrum predicted by the continuum theory not realized.

Engineering, characterization and in vitro efficacy of the major peanut allergens for use in immunotherapy

BACKGROUND

Numerous strategies have been proposed for the treatment of peanut allergies, but despite the steady advancement in our understanding of atopic immune responses and the increasing number of deaths each year from peanut anaphylaxis, there is still no safe, effective, specific therapy for the peanut-sensitive individual. Immunotherapy would be safer and more effective if the allergens could be altered to reduce their ability to initiate an allergic reaction without altering their ability to desensitize the allergic patient.

METHODS

The cDNA clones for three major peanut allergens, Ara h 1, Ara h 2, and Ara h 3, have been cloned and characterized. The IgE-binding epitopes of each of these allergens have been determined and amino acids critical to each epitope identified. Site-directed mutagenesis of the allergen cDNA clones, followed by recombinant production of the modified allergen, provided the reagents necessary to test our hypothesis that hypoallergenic proteins are effective immunotherapeutic reagents for treating peanut-sensitive patients. Modified peanut allergens were subjected to immunoblot analysis using peanut-positive patient sera IgE, T cell proliferation assays, and tested in a murine model of peanut anaphylaxis.

RESULTS

In general, the modified allergens were poor competitors for binding of peanut-specific IgE when compared to their wild-type counterpart. The modified allergens demonstrated a greatly reduced IgE-binding capacity when individual patient serum IgE was compared to the binding capacity of the wild-type allergens. In addition, while there was considerable variability between patients, the modified allergens retained the ability to stimulate T cell proliferation.

CONCLUSIONS

These modified allergen genes and proteins should provide a safe immunotherapeutic agent for the treatment of peanut allergy.

A soybean G2 glycinin allergen. 2. Epitope mapping and three-dimensional modeling

BACKGROUND

Multiple allergens have been documented in soybean extracts. IgE from individuals allergic to soybeans, but not to peanut, has been shown by immunoblot analysis to bind to proteins with a molecular weight of approximately 22 kD. These findings suggested that this unique protein fraction from soybean might be responsible, in part, for soybean allergic reactivity. The objective of the present study was to characterize specific B cell epitopes, to determine if any amino acid was critical to IgE binding and to model the 22-kD G2 soybean allergen to the three-dimensional (3-D) phaseolin molecule.

METHODS

B cell epitopes were identified using SPOTs peptide analysis. Structural orientation of the IgE-binding regions was mapped to the 3-D phaseolin molecule using molecular modeling of the protein tertiary structure.

RESULTS

Eleven linear epitopes, representing 15 amino acid peptide sequences, bound to IgE in the glycinin molecule. These epitopes were predicted to be distributed asymmetrically on the surface of G2 trimers.

CONCLUSIONS

Only 1 epitope could be rendered non-IgE binding by alanine substitutions in the peptide. The nonrandom distribution of the IgE binding sites provides new insight into their organization in trimers in 11S complexes of the G2 glycinin allergen.

A soybean G2 glycinin allergen. 1. Identification and characterization

BACKGROUND

Multiple allergens have been documented in soybean extracts. IgE from individuals allergic to soybeans, but not to peanut, was shown by immunoblot analysis to bind to proteins with a molecular weight of approximately 21 kD. These findings suggested that unique proteins in soybeans might be responsible for soybean allergic reactivity. The objective of the present study was to identify unique proteins in soybean extracts that bind to specific IgE from soybean-sensitive individuals, and to characterize the allergen using physicochemical methods and IgE binding.

METHODS

Two-dimensional and preparative SDS-PAGE/IgE immunoblot analysis was used to identify a 22-kD soybean-specific allergen from crude soybean extracts. N-terminal sequence analysis was used to determine the identification of the protein binding IgE from soybean-sensitive individuals.

RESULTS

IgE immunoblot and amino acid sequence analysis identified the 22-kD protein as a member of the G2 glycinin soybean protein family. Further investigation revealed that the IgEs reacted with basic chains from each member of the glycinin family of soybean storage proteins.

CONCLUSIONS

Each of the subunits from glycinin, the storage protein that is the most prevalent component of soybean, are major allergens.

Mutational analysis of the IgE-binding epitopes of P34/Gly m Bd 30K

BACKGROUND

Peanuts and soybeans are 2 foods that have been shown to be responsible for many atopic disorders. Because of their nutritional benefit, soybean proteins are now being used increasingly in a number of food products. Previous studies have documented multiple allergens in soybean extracts, including glycinin, beta-conglycinin, and the P34/Gly m Bd 30K protein.

OBJECTIVE

Our overall goal was to identify soybean-specific allergens to begin to understand molecular and immunochemical characteristics of legume proteins. The specific aim of the current investigation was to identify the essential amino acid residues necessary for IgE binding in the 5 distinct immunodominant epitopes of P34/Gly m Bd 30K.

METHODS

Serum IgE from 6 clinically sensitive soybean-allergic individuals was used to identify P34/Gly m Bd 30K in the native and single amino acid substituted peptides with use of the SPOTS peptide synthesis technique to determine critical amino acids required for IgE binding.

RESULTS

The intensity of IgE binding and epitope recognition by serum IgE from the individuals varied substantially. With use of serum from 6 clinically soybean-sensitive individuals, 2 of the 5 immunodominant epitopes could be mutagenized to non-IgE binding peptides.

CONCLUSIONS

Single-site amino acid substitution of the 5 immunodominant epitopes of Gly m Bd 30K with alanine revealed that IgE binding could be reduced or eliminated in epitopes 6 and 16 in the serum obtained from 6 soybean-sensitive patients.

Epitope specificity of the major peanut allergen, Ara h II

The antigenic and allergenic structure of Ara h II, a major allergen of peanuts, was investigated with the use of four monoclonal antibodies obtained from BALB/c mice immunized with purified Ara h II. Our previous studies with monoclonal antibodies generated to peanut allergens showed this method to be useful for epitope mapping. When used as a solid phase in an ELISA, these monoclonal antibodies captured peanut antigen, which bound human IgE from patients with positive peanut challenge responses. The Ara h II monoclonal antibodies were found to be specific for peanut antigens when binding for other legumes was examined. In ELISA inhibition studies with the monoclonal antibodies, we identified two different antigenic sites on Ara h II. In similar studies with pooled human IgE serum from patients with positive challenge responses to peanuts, we identified two closely related IgE-binding epitopes. These characterized monoclonal antibodies to Ara h II will be useful for future studies to immunoaffinity purify the Ara h II allergen and to use in conjunction with recombinant technology for determining structure-function relationships.

Identification of peanut agglutinin and soybean trypsin inhibitor as minor legume allergens

Peanuts and soybeans are frequent causes of food hypersensitivity reactions in children. Sera from 12 patients with atopic dermatitis and a positive double-blind placebo-controlled food challenge to peanut and sera from 5 patients with atopic dermatitis and a positive double-blind placebo-controlled food challenge to soybean were used to identify and characterize specific legume allergens. Identification of a minor allergen from peanut and a minor allergen from soybean was accomplished using various physicochemical techniques. The peanut fraction, peanut agglutinin, isolated by anion-exchange chromatography and electrolution and confirmed by amino acid sequencing, bound IgE in only 50% of the peanut challenge positive patients. The soybean fraction, soybean trypsin inhibitor, identified by gel filtration and electroelution and confirmed by amino acid sequencing, bound IgE in only 20% of the soy challenge positive patients. The identification of these two known legume proteins as minor allergens should allow further immunologic and structural investigations to compare the major and minor legume allergens.

Epitope specificity and immunoaffinity purification of the major peanut allergen, Ara h I

The antigenic and allergenic structure of Ara h I, a major allergen of peanuts, was investigated with the use of seven monoclonal antibodies obtained from BALB/c mice immunized with purified Ara h I. Previous work with monoclonal antibodies produced to allergens has primarily been done with inhalant allergens. Only recently have the major allergens of various foods been determined so that investigations with monoclonal antibodies into the allergenic epitopes could begin. When used as a solid phase in an ELISA, these monoclonal antibodies captured peanut antigen, which bound human IgE from patients with positive results to challenges to peanuts. The Ara h I monoclonal antibodies were found to be specific for peanut antigens when binding for other legumes was examined. In ELISA inhibition studies with the monoclonal antibodies, we identified four different antigenic sites on Ara h I. In related studies with pooled human IgE serum from patients with positive results to challenges to peanuts, we identified three similar IgE-binding epitopes. As a means of purifying the Ara h I allergen, we prepared an immunoaffinity column with monoclonal antibody 8D9. We eluted from this column the allergen Ara h I, which had a mean molecular weight of 63.5 kd and which bound human IgE from individual and pooled serum of patients with peanut sensitivity.

Allergenicity of peanut and soybean extracts altered by chemical or thermal denaturation in patients with atopic dermatitis and positive food challenges

Peanuts and soybeans are two of the six most common foods to cause food hypersensitivity reactions in children. We used the serum of 10 patients with atopic dermatitis and positive double-blind, placebo-controlled, food challenges to peanut and two patients with atopic dermatitis and positive double-blind, placebo-controlled, food challenges to soybean to investigate the change in IgE-specific and IgG-specific binding to these proteins altered by either chemical or thermal denaturation. We used IgE- and IgG-specific ELISA-inhibition analyses to compare these effects on the crude peanut and crude soy extracts, as well as on the major allergenic fractions of both proteins. Heating the soy proteins at various temperatures and time intervals did not significantly change the IgE- or IgG-specific binding of the soy positive pooled serum. When the peanut proteins were subjected to similar heating experiments, the IgE- and IgG-specific binding did not change. When these same proteins were treated with enzymes in the immobilized digestive enzyme assay system used to mimic human digestion, the binding of IgE to the crude peanut and crude soy extracts was reduced; 100-fold for peanut and 10-fold for soybean. Therefore it appears that thermal denaturation of peanut and soybean protein extracts does not enhance or reduce IgE- and IgG-specific binding activity. Chemical denaturation appears to minimally reduce the binding of these proteins.

Identification and characterization of a second major peanut allergen, Ara h II, with use of the sera of patients with atopic dermatitis and positive peanut challenge

Peanuts are frequently a cause of food hypersensitivity reactions in children. Serum from nine patients with atopic dermatitis and a positive double-blind, placebo-controlled, food challenge to peanut were used in the process of identification and purification of the peanut allergens. Identification of a second major peanut allergen was accomplished with use of various biochemical and molecular techniques. Anion exchange chromatography of the crude peanut extract produced several fractions that bound IgE from the serum of the patient pool with positive challenges. By measuring antipeanut specific IgE and by IgE-specific immunoblotting we have identified an allergic component that has two closely migrating bands with a mean molecular weight of 17 kd. Two-dimensional gel electrophoresis of this fraction revealed it to have a mean isoelectric point of 5.2. According to allergen nomenclature of the IUIS Subcommittee for Allergen Nomenclature this allergen is designated, Ara h II (Arachis hypogaea).

Effect of soy protein ingestion on total and specific immunoglobulin G concentrations in neonatal porcine serum measured by enzyme-linked immunosorbent assay

Crossbred neonatal pigs from spring and summer farrowings were used to evaluate the systemic humoral immune response in porcine serum after ingestion of soy protein. At 10 to 12 d of age (average BW 3.8 kg), pigs were randomly allotted to three treatment groups according to litter, weight, and sex. Treatments were intermittent gavage feedings two or three times daily for six consecutive days with nonfat dry milk (NFDM) or textured vegetable protein (TVP) and a nongavaged control group. Pigs were weaned at 20 to 22 d of age (average BW 5.7 kg) and fed a corn-soybean meal-based starter diet. Total serum immunoglobulin G (IgG) and IgG concentrations specific for soy protein were measured by ELISA. Blood samples were taken at 1 d of age after colostrum intake and at 4-d intervals from the beginning of treatment to 31 d of age. When averaged from d 1 to 31, spring-farrowed pigs had greater (P less than .005) total IgG and soy-protein-specific IgG concentrations than did summer-farrowed pigs (8.70 vs 6.51 mg/mL and 1.59 vs .55 micrograms/mL, respectively). Total serum IgG concentrations changed with time (P = .005); they initially decreased, then recovered after weaning. These changes were independent of treatment, sex, or farrowing season. Soy-protein-specific IgG concentrations also changed with time (P = .003); however, this trend was dependent on season (P = .014). Summer-farrowed pigs exhibited a more rapid and severe decrease in serum IgG concentrations specific for soy protein than did spring-farrowed pigs.(ABSTRACT TRUNCATED AT 250 WORDS)

Identification of a major peanut allergen, Ara h I, in patients with atopic dermatitis and positive peanut challenges

Peanuts are among the most common causes of immediate hypersensitivity reactions to foods. Serum from nine patients with atopic dermatitis and a positive double-blind, placebo-controlled, food challenge to peanut were used to begin the process of identification and purification of the major peanut allergens. Identification of a major peanut allergen was accomplished by use of anion-exchange column chromatography, sodium dodecyl sulfate-polyacrylamide gel electrophoresis, ELISA, thin-layer isoelectric focusing, and IgE-specific immunoblotting. Anion-exchange chromatography revealed several fractions that bound IgE from the serum of the challenge-positive patient pool. By measuring antipeanut-specific IgE in the ELISA and in IgE-specific immunoblotting, we identified an allergenic component with two Coomassie brilliant blue staining bands by sodium dodecyl sulfate-polyacrylamide gel electrophoresis with a mean molecular weight of 63.5 kd. Examination of this fraction by the IgE antipeanut ELISA with individual serum and by the ELISA-inhibition assay with pooled serum, we identified this fraction as a major allergen. Thin-layer isoelectric focusing and immunoblotting of this 63.5 kd fraction revealed it to have an isoelectric point of 4.55. Based on allergen nomenclature of the IUIS Subcommittee for Allergen Nomenclature, this allergen is designated, Ara h I (Arachis hypogaea).

Enzyme-linked immunosorbent assay and immunoblotting determination of antibody response to major component proteins of soybeans in patients with soy protein intolerance

To evaluate the role of food-specific antibody response to soy protein and its fractions in patients with soy protein intolerance, allergen-specific IgG and IgE to these proteins were measured by enzyme-linked immunosorbent assays (ELISAS) and immunoblotting. A crude soy extract and the 7S, 11S, and whey fractions were isolated from commercial defatted soy flakes. Of 23 patients who underwent standardized soy challenges, seven were positive. The ELISA results showed no statistically different responses between the challenge-positive and challenge-negative groups in IgG or IgE specific to either the crude soy extract or the 7S, 11S, and whey fractions (p greater than .3 for all variables). In comparing the soy-positive patients, the level of IgE specific for 7S and 11S was significantly different compared with whey; the level of IgE specific for crude soy extract and 7S was significantly different compared with whey. The immunoblots reveal that IgG and IgE are present in varying amounts to multiple fractions of the soy protein. The study does not provide evidence for a pathogenic role of serum food-specific antibodies in soy protein intolerance.