An ensemble forecast system for tracking dynamics of dengue outbreaks and its validation in China

As a common vector-borne disease, dengue fever remains challenging to predict due to large variations in epidemic size across seasons driven by a number of factors including population susceptibility, mosquito density, meteorological conditions, geographical factors, and human mobility. An ensemble forecast system for dengue fever is first proposed that addresses the difficulty of predicting outbreaks with drastically different scales. The ensemble forecast system based on a susceptible-infected-recovered (SIR) type of compartmental model coupled with a data assimilation method called the ensemble adjusted Kalman filter (EAKF) is constructed to generate real-time forecasts of dengue fever spread dynamics. The model was informed by meteorological and mosquito density information to depict the transmission of dengue virus among human and mosquito populations, and generate predictions. To account for the dramatic variations of outbreak size in different seasons, the effective population size parameter that is sequentially updated to adjust the predicted outbreak scale is introduced into the model. Before optimizing the transmission model, we update the effective population size using the most recent observations and historical records so that the predicted outbreak size is dynamically adjusted. In the retrospective forecast of dengue outbreaks in Guangzhou, China during the 2011–2017 seasons, the proposed forecast model generates accurate projections of peak timing, peak intensity, and total incidence, outperforming a generalized additive model approach. The ensemble forecast system can be operated in real-time and inform control planning to reduce the burden of dengue fever.     

Niche theory‐based modeling of assembly processes of viral communities in bats

Understanding the assembly processes of symbiont communities, including viromes and microbiomes, is important for improving predictions on symbionts’ biogeography and disease ecology. Here, we use phylogenetic, functional, and geographic filters to predict the similarity between symbiont communities, using as a test case the assembly process in viral communities of Mexican bats. We construct generalized linear models to predict viral community similarity, as measured by the Jaccard index, as a function of differences in host phylogeny, host functionality, and spatial co‐occurrence, evaluating the models using the Akaike information criterion. Two model classes are constructed: a “known” model, where virus–host relationships are based only on data reported in Mexico, and a “potential” model, where viral reports of all the Americas are used, but then applied only to bat species that are distributed in Mexico. Although the “known” model shows only weak dependence on any of the filters, the “potential” model highlights the importance of all three filter types—phylogeny, functional traits, and co‐occurrence—in the assemblage of viral communities. The differences between the “known” and “potential” models highlight the utility of modeling at different “scales” so as to compare and contrast known information at one scale to another one, where, for example, virus information associated with bats is much scarcer.     

The impact of Cenozoic cooling on assemblage diversity in planktonic foraminifera

The Cenozoic planktonic foraminifera (PF) (calcareous zooplankton) have arguably the most detailed fossil record of any group. The quality of this record allows models of environmental controls on macroecology, developed for Recent assemblages, to be tested on intervals with profoundly different climatic conditions. These analyses shed light on the role of long-term global cooling in establishing the modern latitudinal diversity gradient (LDG)—one of the most powerful generalizations in biogeography and macroecology. Here, we test the transferability of environment-diversity models developed for modern PF assemblages to the Eocene epoch (approx. 56–34 Ma), a time of pronounced global warmth. Environmental variables from global climate models are combined with Recent environment–diversity models to predict Eocene richness gradients, which are then compared with observed patterns. The results indicate the modern LDG—lower richness towards the poles—developed through the Eocene. Three possible causes are suggested for the mismatch between statistical model predictions and data in the Early Eocene: the environmental estimates are inaccurate, the statistical model misses a relevant variable, or the intercorrelations among facets of diversity—e.g. richness, evenness, functional diversity—have changed over geological time. By the Late Eocene, environment–diversity relationships were much more similar to those found today.     

Learning to Estimate Dynamical State with Probabilistic Population Codes

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.     

Near-term forecasting of companion animal tick paralysis incidence: An iterative ensemble model

Tick paralysis resulting from bites from Ixodes holocyclus and I. cornuatus is one of the leading causes of emergency veterinary admissions for companion animals in Australia, often resulting in death if left untreated. Availability of timely information on periods of increased risk can help modulate behaviors that reduce exposures to ticks and improve awareness of owners for the need of lifesaving preventative ectoparasite treatment. Improved awareness of clinicians and pet owners about temporal changes in tick paralysis risk can be assisted by ecological forecasting frameworks that integrate environmental information into statistical time series models. Using an 11-year time series of tick paralysis cases from veterinary clinics in one of Australia’s hotspots for the paralysis tick Ixodes holocyclus, we asked whether an ensemble model could accurately forecast clinical caseloads over near-term horizons. We fit a series of statistical time series (ARIMA, GARCH) and generative models (Prophet, Generalised Additive Model) using environmental variables as predictors, and then combined forecasts into a weighted ensemble to minimise prediction interval error. Our results indicate that variables related to temperature anomalies, levels of vegetation moisture and the Southern Oscillation Index can be useful for predicting tick paralysis admissions. Our model forecasted tick paralysis cases with exceptional accuracy while preserving epidemiological interpretability, outperforming a field-leading benchmark Exponential Smoothing model by reducing both point and prediction interval errors. Using online particle filtering to assimilate new observations and adjust forecast distributions when new data became available, our model adapted to changing temporal conditions and provided further reduced forecast errors. We expect our model pipeline to act as a platform for developing early warning systems that can notify clinicians and pet owners about heightened risks of environmentally driven veterinary conditions.     

The Effect of Hydraulic Loading Rate and Influent Source on the Binding Capacity of Phosphorus Filters

Sorption by active filter media can be a convenient option for phosphorus (P) removal and recovery from wastewater for on-site treatment systems. There is a need for a robust laboratory method for the investigation of filter materials to enable a reliable estimation of their longevity. The objectives of this study were to (1) investigate and (2) quantify the effect of hydraulic loading rate and influent source (secondary wastewater and synthetic phosphate solution) on P binding capacity determined in laboratory column tests and (3) to study how much time is needed for the P to react with the filter material (reaction time). To study the effects of these factors, a 2² factorial experiment with 11 filter columns was performed. The reaction time was studied in a batch experiment. Both factors significantly (α = 0.05) affected the P binding capacity negatively, but the interaction of the two factors was not significant. Increasing the loading rate from 100 to 1200 L m⁻² d⁻¹ decreased P binding capacity from 1.152 to 0.070 g kg⁻¹ for wastewater filters and from 1.382 to 0.300 g kg⁻¹ for phosphate solution filters. At a loading rate of 100 L m⁻² d⁻¹, the average P binding capacity of wastewater filters was 1.152 g kg⁻¹ as opposed to 1.382 g kg⁻¹ for phosphate solution filters. Therefore, influent source or hydraulic loading rate should be carefully controlled in the laboratory. When phosphate solution and wastewater were used, the reaction times for the filters to remove P were determined to be 5 and 15 minutes, respectively, suggesting that a short residence time is required. However, breakthrough in this study occurred unexpectedly quickly, implying that more time is needed for the P that has reacted to be physically retained in the filter.     

Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics.     

Using Mendelian Inheritance To Improve High-Throughput SNP Discovery

Restriction site-associated DNA sequencing or genotyping-by-sequencing (GBS) approaches allow for rapid and cost-effective discovery and genotyping of thousands of single-nucleotide polymorphisms (SNPs) in multiple individuals. However, rigorous quality control practices are needed to avoid high levels of error and bias with these reduced representation methods. We developed a formal statistical framework for filtering spurious loci, using Mendelian inheritance patterns in nuclear families, that accommodates variable-quality genotype calls and missing data—both rampant issues with GBS data—and for identifying sex-linked SNPs. Simulations predict excellent performance of both the Mendelian filter and the sex-linkage assignment under a variety of conditions. We further evaluate our method by applying it to real GBS data and validating a subset of high-quality SNPs. These results demonstrate that our metric of Mendelian inheritance is a powerful quality filter for GBS loci that is complementary to standard coverage and Hardy–Weinberg filters. The described method, implemented in the software MendelChecker, will improve quality control during SNP discovery in nonmodel as well as model organisms.     

FOXO4 peptide targets myofibroblast ameliorates bleomycin‐induced pulmonary fibrosis in mice through ECM‐receptor interaction pathway

Pulmonary fibrosis (PF) is a progressive interstitial lung disease with limited treatment options. The incidence and prevalence of PF is increasing with age, cell senescence has been proposed as a pathogenic driver, the clearance of senescent cells could improve lung function in PF. FOXO4‐D‐Retro‐Inverso (FOXO4‐DRI), a synthesis peptide, has been reported to selectively kill senescent cells in aged mice. However, it remains unknown if FOXO4‐DRI could clear senescent cells in PF and reverse this disease. In this study, we explored the effect of FOXO4‐DRI on bleomycin (BLM)‐induced PF mouse model. We found that similar as the approved medication Pirfenidone, FOXO4‐DRI decreased senescent cells, downregulated the expression of senescence‐associated secretory phenotype (SASP) and attenuated BLM‐induced morphological changes and collagen deposition. Furthermore, FOXO4‐DRI could increase the percentage of type 2 alveolar epithelial cells (AEC2) and fibroblasts, and decrease the myofibroblasts in bleomycin (BLM)‐induced PF mouse model. Compared with mouse and human lung fibroblast cell lines, FOXO4‐DRI is inclined to kill TGF‐β‐induced myofibroblast in vitro. The inhibited effect of FOXO4‐DRI on myofibroblast lead to a downregulation of extracellular matrix (ECM) receptor interaction pathway in BLM‐induced PF. Above all, FOXO4‐DRI ameliorates BLM‐induced PF in mouse and may be served as a viable therapeutic option for PF.     

Evaluation of Five Membrane Filtration Methods for Recovery of Cryptosporidium and Giardia Isolates from Water Samples

We evaluated the efficiency of five membrane filters for recovery of Cryptosporidium parvum oocysts and Giardia lamblia cysts. These filters included the Pall Life Sciences Envirochek (EC) standard filtration and Envirochek high-volume (EC-HV) membrane filters, the Millipore flatbed membrane filter, the Sartorius flatbed membrane filter (SMF), and the Filta-Max (FM) depth filter. Distilled and surface water samples were spiked with 10 oocysts and 10 cysts/liter. We also evaluated the recovery efficiency of the EC and EC-HV filters after a 5-s backwash postfiltration. The backwashing was not applied to the other filtration methods because of the design of the filters. Oocysts and cysts were visualized by using a fluorescent monoclonal antibody staining technique. For distilled water, the highest percent recovery for both the oocysts and cysts was obtained with the FM depth filter. However, when a 5-s backwash was applied, the EC-HV membrane filter (EC-HV-R) was superior to other filters for recovery of both oocysts (n = 53 ± 15.4 per 10 liters) and cysts (n = 59 ± 11.5 per 10 liters). This was followed by results of the FM depth filter (oocysts, 28.2 ± 8, P = 0.015; cysts, 49.8 ± 12.2, P = 0.4260), and SMF (oocysts, 16.2 ± 2.8, P = 0.0079; cysts, 35.2 ± 3, P = 0.0079). Similar results were obtained with surface water samples. Giardia cysts were recovered at higher rates than were Cryptosporidium oocysts with all five filters, regardless of backwashing. Although the time differences for completion of filtration process were not significantly different among the procedures, the EC-HV filtration with 5-s backwash was less labor demanding.     

Playing together,laughing together: rapid facial mimicry and social sensitivity in lowland gorillas

In nonhuman animals, the phenomenon of rapid facial mimicry (RFM)—the automatic, involuntary, and rapid (<1 s) replication of others’ facial expressions—has been mainly investigated in the playful domain. In immature lowland gorillas Gorilla gorilla gorilla both play face (PF) and full PF (FPF) are rapidly mimicked between the players. This makes the species suitable to test hypotheses on the factors influencing RFM during play. The observations on 3 captive groups of lowland gorillas (N = 27) revealed that contrary to expectations, the closeness of social bond negatively influenced the occurrence of RFM but it did not affect either RFM latency or its overlapping index (OVERLAP). RFM was affected by the degree of symmetry of play fighting: the more balanced the session, the higher the occurrence of RFM. Players of the same sex class responded faster than players of different sex. These findings suggest that RFM may help synchronizing behaviors of playmates matching in size (same-sex) and promote symmetric playful interactions. “Laughing together” (measured by the RFM OVERLAP) lasted longer when the responder perfectly mirrored the partner expression (PF→PF; FPF→FPF). If PF and FPF convey information on the different play roughness degree, through “laughing together” the players could coordinate their actions and share positive moods and playful intensity. If the perfect congruency in the motor resonance, also known as social sensitivity, can foster a possible emotional dialogue between gorillas remains to be investigated.     

Biological expressions of lymphocyte activation. V. Characterization of a soluble immune response suppressor (SIRS) produced by concanavalin A-activated spleen cells.

Supernatant fluids from murine spleen cell cultures incubated with concanavalin A for 48 hr contain a factor(s), soluble immune response suppressor (SIRS), which suppresses plaque-forming cell responses to sheep erythrocytes by murine spleen cells in vitro. In the present studies, some of the biochemical and biophysical properties of SIRS were investigated. SIRS was non-dialysable; the suppressive activity was stable at 56 degrees C for 30 min, but was destroyed by treatment at 70 degrees C for 30 min, 80 degrees C for 10 min, or at pH 2. The suppressive activity was not absorbed by the stimulating antigen, SRBC, or antisera against murine IgG or mu-chain, suggesting that SIRS does not contain immunoglobulin determinants. Murine spleen and thymus, but not kidney cells, however, absorbed SIRS activity. Enzyme treatments revealed that SIRS was resistant to DNase and RNase, but was destroyed by trypsin and chymotrypsin. In gel filtration with Sephadex G-100, SIRS activity eluted in the fraction corresponding to m.w. in the range between 48,000 and 67,000. With polyacrylamide gel electrophoresis, SIRS activity migrated in the region cathodal to albumin. Isopycnic centrifugation in a cesium chloride gradient suggested that SIRS is a glycoprotein. These supernatant fluids with SIRS activity were also found to contain macrophage migration inhibitory factor (MIF). In the experiments using gel filtration, polyacrylamide gel electrophoresis, and isopycnic centrifugation to fractionate supernatant fluids, SIRS and MIF activity were found in the same fractions, and to date we have been unable to dissociate definitively SIRS activity from MIF activity.     

Using Extreme Value Theory Approaches to Forecast the Probability of Outbreak of Highly Pathogenic Influenza in Zhejiang,China

BackgroundInfluenza is a contagious disease with high transmissibility to spread around the world with considerable morbidity and mortality and presents an enormous burden on worldwide public health. Few mathematical models can be used because influenza incidence data are generally not normally distributed. We developed a mathematical model using Extreme Value Theory (EVT) to forecast the probability of outbreak of highly pathogenic influenza.MethodsThe incidence data of highly pathogenic influenza in Zhejiang province from April 2009 to November 2013 were retrieved from the website of Health and Family Planning Commission of Zhejiang Province. MATLAB “VIEM” toolbox was used to analyze data and modelling. In the present work, we used the Peak Over Threshold (POT) model, assuming the frequency as a Poisson process and the intensity to be Pareto distributed, to characterize the temporal variability of the long-term extreme incidence of highly pathogenic influenza in Zhejiang, China.ResultsThe skewness and kurtosis of the incidence of highly pathogenic influenza in Zhejiang between April 2009 and November 2013 were 4.49 and 21.12, which indicated a “fat tail” distribution. A QQ plot and a mean excess plot were used to further validate the features of the distribution. After determining the threshold, we modeled the extremes and estimated the shape parameter and scale parameter by the maximum likelihood method. The results showed that months in which the incidence of highly pathogenic influenza is about 4462/2286/1311/487 are predicted to occur once every five/three/two/one year, respectively.ConclusionsDespite the simplicity, the present study successfully offers the sound modeling strategy and a methodological avenue to implement forecasting of an epidemic in the midst of its course.     

Evaluation of Restoration Practice Based on Environmental Filters

Environmental filter models have been proposed as conceptual organizing frameworks for comparing and contrasting restoration practices. I evaluate two such environmental filter models, one proposed by Fattorini and Halle (2004) and the other by Hobbs and Norton (2004) . These models were developed by abstracting restoration practice into what the authors viewed as the essential features restoration practitioners target for control or manipulation. In so doing, these conceptual frameworks hope to be able to transfer insights between different kinds of ecosystems. Here, I take the opposite approach: given an environmental filter model, I asked how well its filters could characterize restoration practices reported in the literature. I found that it was easier to characterize specific restoration practice using the more detailed filters described by Hobbs and Norton. I found that manipulation of biotic filters was most common in terrestrial ecosystems, whereas manipulation of abiotic filters was more common in wetland and stream ecosystems. Fattorini and Halle’s model appears most useful for evaluating the current status of degraded ecosystems compared to nondegraded ones, but Hobbs and Norton’s model is better for evaluating what particular restoration activities might be undertaken to move that system from a degraded to a nondegraded state.     

State Estimation of the Time-Varying and Spatially Localized Concentration of Signal Molecules from the Stochastic Adsorption Dynamics on the Carbon Nanotube-Based Sensors and Its Application to Tumor Cell Detection

This paper addresses a problem of estimating time-varying, local concentrations of signal molecules with a carbon-nanotube (CNT)-based sensor array system, which sends signals triggered by monomolecular adsorption/desorption events of proximate molecules on the surfaces of the sensors. Such sensors work on nano-scale phenomena and show inherently stochastic non-Gaussian behavior, which is best represented by the chemical master equation (CME) describing the time evolution of the probabilities for all the possible number of adsorbed molecules. In the CME, the adsorption rate on each sensor is linearly proportional to the local concentration in the bulk phase. State estimators are proposed for these types of sensors that fully address their stochastic nature. For CNT-based sensors motivated by tumor cell detection, the particle filter, which is nonparametric and can handle non-Gaussian distributions, is compared to a Kalman filter that approximates the underlying distributions by Gaussians. In addition, the second-order generalized pseudo Bayesian estimation (GPB2) algorithm and the Markov chain Monte Carlo (MCMC) algorithm are incorporated into KF and PF respectively, for detecting latent drift in the concentration affected by different states of a cell.     

State estimation of a Uricase production process with Candida Utilis

The present work discusses the implementation of a Kalman filtering procedure in a state estimation of a batch Uricase production process with Candida Utilis. An unstructured model of the process is used for the estimation procedure. The observability is thoroughly investigated and a Kalman filter is applied afterwards as a powerful and precise state estimation tool. The estimates in all cases of observability are presented, compared and discussed.     

Estimating joint kinematics from skin motion observation: modelling and validation

Modelling of soft tissue motion is required in many areas, such as computer animation, surgical simulation, 3D motion analysis and gait analysis. In this paper, we will focus on the use of modelling of skin deformation during 3D motion analysis. The most frequently used method in 3D human motion analysis involves placing markers on the skin of the analysed segment which is composed of the rigid bone and the surrounding soft tissues. Skin and soft tissue deformations introduce a significant artefact which strongly influences the resulting bone position, orientation and joint kinematics. For this study, we used a statistical solid dynamics approach which is a combination of several previously reported tools: the point cluster technique (PCT) and a Kalman filter which was added to the PCT. The methods were tested and evaluated on controlled human-arm motions, using an optical motion capture system (Vicon^TM).

The addition of a Kalman filter to the PCT for rigid body motion estimation results in a smoother signal that better represents the joint motion. Calculations indicate less signal distortion than when using a digital low-pass filter. Furthermore, adding a Kalman filter to the PCT substantially reduces the dispersion of the maximal and minimal instantaneous frequencies. For controlled human movements, the result indicated that adding a Kalman filter to the PCT produced a more accurate signal. However, it could not be concluded that the proposed Kalman filter is better than a low-pass filter for estimation of the motion. We suggest that implementation of a Kalman filter with a better biomechanical motion model will be more likely to improve the results.     

Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information

Abstract

The extended Kalman filter (EKF) has been applied to inferring gene regulatory networks. However, it is well known that the EKF becomes less accurate when the system exhibits high nonlinearity. In addition, certain prior information about the gene regulatory network exists in practice, and no systematic approach has been developed to incorporate such prior information into the Kalman-type filter for inferring the structure of the gene regulatory network. In this paper, an inference framework based on point-based Gaussian approximation filters that can exploit the prior information is developed to solve the gene regulatory network inference problem. Different point-based Gaussian approximation filters, including the unscented Kalman filter (UKF), the third-degree cubature Kalman filter (CKF₃), and the fifth-degree cubature Kalman filter (CKF₅) are employed. Several types of network prior information, including the existing network structure information, sparsity assumption, and the range constraint of parameters, are considered, and the corresponding filters incorporating the prior information are developed. Experiments on a synthetic network of eight genes and the yeast protein synthesis network of five genes are carried out to demonstrate the performance of the proposed framework. The results show that the proposed methods provide more accurate inference results than existing methods, such as the EKF and the traditional UKF.

     

Influenza virus in human exhaled breath: an observational study

Background

Recent studies suggest that humans exhale fine particles during tidal breathing but little is known of their composition, particularly during infection.

Methodology/Principal Findings

We conducted a study of influenza infected patients to characterize influenza virus and particle concentrations in their exhaled breath. Patients presenting with influenza-like-illness, confirmed influenza A or B virus by rapid test, and onset within 3 days were recruited at three clinics in Hong Kong, China. We collected exhaled breath from each subject onto Teflon filters and measured exhaled particle concentrations using an optical particle counter. Filters were analyzed for influenza A and B viruses by quantitative polymerase chain reaction (qPCR). Twelve out of thirteen rapid test positive patients provided exhaled breath filter samples (7 subjects infected with influenza B virus and 5 subjects infected with influenza A virus). We detected influenza virus RNA in the exhaled breath of 4 (33%) subjects–three (60%) of the five patients infected with influenza A virus and one (14%) of the seven infected with influenza B virus. Exhaled influenza virus RNA generation rates ranged from <3.2 to 20 influenza virus RNA particles per minute. Over 87% of particles exhaled were under 1 µm in diameter.

Conclusions

These findings regarding influenza virus RNA suggest that influenza virus may be contained in fine particles generated during tidal breathing, and add to the body of literature suggesting that fine particle aerosols may play a role in influenza transmission.