In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Q: Estimating inhalation dose accurately under realistic conditions can enhance the accuracy of risk assessment. Conventional methods to quantify aerosol concentration that susceptible victims in contaminated environments are exposed to use real time particle counters to measure concentrations in environments without occupancy. Breathing-induced airflow interacts and influences concentration around nostrils or mouth and alter the ultimate exposure. This subject has not yet been systematically studied, particularly under transient emission. In this work, an experimental facility comprising two manikins was designed and fabricated. One of them mimicked realistic breathing, acting as a susceptible victim. Both steady and episodic emissions were generated in an air-conditioned environmental chamber in which two different ventilation schemes were tested. The scaled-dose of the victim under different expiratory velocities and pulmonary ventilation was measured. Inferring from results obtained from comprehensive tests, it can be concluded that breathing has very significant influence on the ultimate dose compared with that without breathing. Majority of results show that breathing reduces inhalation quantity and the reduction magnitude increases with breathing rate. This is attributed to the fact that the exhalation process plays a more significant role in reducing the dose level than the enhanced effect during inhalation period. The higher the breathing rate, the sharper the decline of the resultant concentration would be leading to lower dose. Nevertheless, under low pulmonary ventilation, results show that breathing increases dose marginally. Results also reveals that ventilation scheme also affects the exposure.

A: An experimental study quantifying pulmonary ventilation on inhalation of aerosol under steady and episodic emission
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Q: Overlapping genes represent a fascinating evolutionary puzzle, since they encode two functionally unrelated proteins from the same DNA sequence. They originate by a mechanism of overprinting, in which point mutations in an existing frame allow the expression (the "birth") of a completely new protein from a second frame. In viruses, in which overlapping genes are abundant, these new proteins often play a critical role in infection, yet they are frequently overlooked during genome annotation. This results in erroneous interpretation of mutational studies and in a significant waste of resources. Therefore, overlapping genes need to be correctly detected, especially since they are now thought to be abundant also in eukaryotes. Developing better detection methods and conducting systematic evolutionary studies require a large, reliable benchmark dataset of known cases. We thus assembled a highquality dataset of 80 viral overlapping genes whose expression is experimentally proven. Many of them were not present in databases. We found that overall, overlapping genes differ significantly from non-overlapping genes in their nucleotide and amino acid composition. In particular, the proteins they encode are enriched in high-degeneracy amino acids and depleted in low-degeneracy ones, which may alleviate the evolutionary constraints acting on overlapping genes. Principal component analysis revealed that the vast majority of overlapping genes follow a similar composition bias, despite their heterogeneity in length and function. Six proven mammalian overlapping genes also followed this bias. We propose that this apparently near-universal composition bias may either favour the birth of overlapping genes, or/and result from selection pressure acting on them.

A: Overlapping genes and the proteins they encode differ significantly in their sequence composition from non-overlapping genes
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Q: Bovine respiratory disease complex is a major cause of illness in dairy calves. The diagnosis of active infection of the lower respiratory tract is challenging on daily basis in the absence of accurate clinical signs. Clinical scoring systems such as the Californian scoring system, are appealing but were developed without considering the imperfection of reference standard tests used for case definition. This study used a Bayesian latent class model to update Californian prediction rules. The results of clinical examination and ultrasound findings of 608 preweaned dairy calves were used. A model accounting for imperfect accuracy of thoracic ultrasound examination was used to obtain updated weights for the clinical signs included in the Californian scoring system. There were 20 points (95% Bayesian credible intervals: 11-29) for abnormal breathing pattern, 16 points (95% BCI: 4-29) for ear drop/head tilt, 16 points (95% BCI: 9-25) for cough, 10 points (95% BCI: 3-18) for the presence of nasal discharge, 7 points (95% BCI: −1 to 8) for rectal temperature ≥39.2°C, and −1 points (95% BCI: −9 to 8) for the presence of ocular discharge. The optimal cut-offs were determined using the misclassification cost-term term (MCT) approach with different possible scenarios of expected prevalence and different plausible ratio of false negative costs/false positive costs. The predicted probabilities of active infection of the lower respiratory tract were also obtained using posterior densities of the main logistic regression model. Depending on the context, cut-off varying from 9 to 16 can minimized the MCT. The optimal cut-off decreased when expected prevalence of disease and false negative/false positive ratio increased.

A:
Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework
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