Recent studies have indicated that COVID-19 is an airborne disease, which has driven conservative social distancing and widescale usage of face coverings. Airborne virus transmission occurs through droplets formed during respiratory events (breathing, speaking, coughing, and sneezing) associated with the airflow through a network of nasal and buccal passages. The airflow interacts with saliva/mucus films where droplets are formed and dispersed, creating a route to transmit SARS-CoV-2. Here, we present a series of numerical simulations to investigate droplet dispersion from a sneeze while varying a series of human physiological factors that can be associated with illness, anatomy, stress condition, and sex of an individual. The model measures the transmission risk utilizing an approximated upper respiratory tract geometry for the following variations: (1) the effect of saliva properties and (2) the effect of geometric features within the buccal/nasal passages. These effects relate to natural human physiological responses to illness, stress, and sex of the host as well as features relating to poor dental health. The results find that the resulting exposure levels are highly dependent on the fluid dynamics that can vary depending on several human factors. For example, a sneeze without flow in the nasal passage (consistent with congestion) yields a 300% rise in the droplet content at 1.83 m (≈6 ft) and an increase over 60% on the spray distance 5 s after the sneeze. Alternatively, when the viscosity of the saliva is increased (consistent with the human response to illness), the number of droplets is both fewer and larger, which leads to an estimated 47% reduction in the transmission risk. These findings yield novel insight into variability in the exposure distance and indicate how physiological factors affect transmissibility rates. Such factors may partly relate to how the immune system of a human has evolved to prevent transmission or be an underlying factor driving superspreading events in the COVID-19 pandemic.

Background: Protein-protein interactions (PPI) can be classified according to their characteristics into, for example obligate or transient interactions. The identification and characterization of these PPI types may help in the functional annotation of new protein complexes and in the prediction of protein interaction partners by knowledge driven approaches.

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A new procedure has been suggested and applied to the simultaneous correlation of the experimental data corresponding to all equilibrium regions in ternary systems involving a solid compound. The analysis of the form of the Gibbs energy function permits the validity of the parameters calculated with any particular model to be verified and can be used as a consistence criterion.

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SummaryThis research addresses the problem of prediction of protein-protein interactions (PPI) when integrating diverse kinds of biological information. This task has been commonly viewed as a binary classification problem (whether any two proteins do or do not interact) and several different machine learning techniques have been employed to solve this task. However the nature of the data creates two major problems which can affect results. These are firstly imbalanced class problems due to the number of positive examples (pairs of proteins which really interact) being much smaller than the number of negative ones. Secondly the selection of negative examples can be based on some unreliable assumptions which could introduce some bias in the classification results. Here we propose the use of one-class classification (OCC) methods to deal with the task of prediction of PPI. OCC methods utilise examples of just one class to generate a predictive model which consequently is independent of the kind of negative examples selected; additionally these approaches are known to cope with imbalanced class problems. We have designed and carried out a performance evaluation study of several OCC methods for this task, and have found that the Parzen density estimation approach outperforms the rest. We also undertook a comparative performance evaluation between the Parzen OCC method and several conventional learning techniques, considering different scenarios, for example varying the number of negative examples used for training purposes. We found that the Parzen OCC method in general performs competitively with traditional approaches and in many situations outperforms them. Finally we evaluated the ability of the Parzen OCC approach to predict new potential PPI targets, and validated these results by searching for biological evidence in the literature.

With an increasing body of evidence that SARS-CoV-2 is an airborne pathogen, droplet character formed during speech, coughs, and sneezes are important. Larger droplets tend to fall faster and are less prone to drive the airborne transmission pathway. Alternatively, small droplets (aerosols) can remain suspended for long time periods. The small size of SARS-CoV-2 enables it to be encapsulated in these aerosols, thereby increasing the pathogen’s ability to be transmitted via airborne paths. Droplet formation during human respiratory events relates to airspeed (speech, cough, sneeze), fluid properties of the saliva/mucus, and the fluid content itself. In this work, we study the fluidic drivers (fluid properties and content) and their influence on factors relating to transmissibility. We explore the relationship between saliva fluid properties and droplet airborne transmission paths. Interestingly, the natural human response appears to potentially work with these drivers to mitigate pathogen transmission. In this work, the saliva is varied using two approaches: (1) modifying the saliva with colloids that increase the viscosity/surface tension, and (2) stimulating the saliva content to increased/decreased levels. Through modern experimental and numerical flow diagnostic methods, the character, content, and exposure to droplets and aerosols are all evaluated. The results indicate that altering the saliva properties can significantly impact the droplet size distribution, the formation of aerosols, the trajectory of the bulk of the droplet plume, and the exposure (or transmissibility) to droplets. High-fidelity numerical methods used and verify that increased droplet size character enhances droplet fallout. In the context of natural saliva response, we find previous studies indicating natural human responses of increased saliva viscosity from stress and reduced saliva content from either stress or illness. These responses both favorably correspond to reduced transmissibility. Such a finding also relates to potential control methods, hence, we compared results to a surgical mask. In general, we find that saliva alteration can produce fewer and larger droplets with less content and aerosols. Such results indicate a novel approach to alter SARS-CoV-2’s transmission path and may act as a way to control the COVID-19 pandemic, as well as influenza and the common cold.