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.

Example Input: 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.
Example Output: An experimental study quantifying pulmonary ventilation on inhalation of aerosol under steady and episodic emission

Example Input: Dendritic cells (DCs) are recognized as highly potent antigen-presenting cells that are able to stimulate cytotoxic T lymphocyte (CTL) responses with antitumor activity. Consequently, DCs have been explored as cellular vaccines in cancer immunotherapy. To that end, DCs are modified with tumor antigens to enable presentation of antigen-derived peptides to CTLs. In this review we discuss the use of viral vectors for in situ modification of DCs, focusing on their clinical applications as anticancer vaccines. Among the viral vectors discussed are those derived from viruses belonging to the families of the Poxviridae, Adenoviridae, Retroviridae, Togaviridae, Paramyxoviridae, and Rhabdoviridae. We will further shed light on how the combination of viral vector-based vaccination with T-cell supporting strategies will bring this strategy to the next level. Since their discovery in 1973, it was clear that dendritic cells (DCs) stood out above the immune cell pack (1, 2). They are morphologically distinct from all other immune cell types and are gifted with an unparalleled capacity to take up, process and present self and foreign antigens to both CD4 + and CD8 + T cells. DCs are critical intermediaries between the innate and adaptive immune systems, as they stimulate, regulate, and shape both immunity and tolerance in all its disguises. Ralph Steinmann, who discovered these cells, was awarded the Nobel Prize for Medicine in 2011, because the discovery of DCs changed medicine (3). Dendritic cells in both humans and mice represent a population of at least four different subtypes with distinct phenotypical and functional characteristics (4-7). These subsets are: plasmacytoid DCs (pDCs), two subsets of conventional DCs (cDC1 and cDC2), and inflammatory DCs. The latter represent a monocyte-derived subset that appears during inflammatory responses (Table 1) . Recently, additional types of human blood DCs, monocytes, and progenitors were revealed using single cell RNA-sequencing. The group of Prof. Nir Hacohen identified pDCs next to cDC progenitor-derived cDC1 (Clec9A + ) and two types of CD1c + cDC2, of which one can also be derived from CD14 + DCs. Furthermore they found a CD141 − CD1c − CD11c + DC subset derived from CD16 + monocytes and an AXL + Siglec6 + subset (8). Future research will have to unravel a possible murine representative for the human cDC2 and AXL + Siglec6 + DC subset. Also, Langerhans cells have been considered an important DC subset for vaccination as they are localized in the epidermis (HLA-DR + CD11c + CD1a + CD207 + ). However, recent evidence suggests that they are related to macrophages, another antigen-presenting cell (APC) type with potential antitumor activity (9).
Example Output: The Journey of in vivo Virus Engineered Dendritic Cells From Bench to Bedside: A Bumpy Road

Example Input: Unbiased metagenomic sequencing holds significant potential as a diagnostic tool for the simultaneous detection of any previously genetically described viral nucleic acids in clinical samples. Viral genome sequences can also inform on likely phenotypes including drug susceptibility or neutralization serotypes. In this study, different variables of the laboratory methods often used to generate viral metagenomics libraries were compared for their abilities to detect multiple viruses and generate full genome coverage. A biological reagent consisting of 25 different human RNA and DNA viral pathogens was used to estimate the effect of filtration and nuclease digestion, DNA/RNA extraction methods, pre-amplification and the use of different library preparation kits on the detection of viral nucleic acids. Filtration and nuclease treatment led to slight decreases in the percentage of viral sequence reads and number of viruses detected. For nucleic acid extractions silica spin columns improved viral sequence recovery relative to magnetic beads and Trizol extraction. Pre-amplification using random RT-PCR while generating more viral sequence reads resulted in detection of fewer viruses, more overlapping sequences, and lower genome coverage. The ScriptSeq library preparation method retrieved more viruses and a greater fraction of their genomes than the TruSeq and Nextera methods. Viral metagenomics sequencing was able to simultaneously detect up to 22 different viruses in the biological reagent analyzed including all those detected by qPCR. Further optimization will be required for the detection of viruses in biologically more complex samples such as tissues, blood, or feces.
Example Output:
Comparing viral metagenomics methods using a highly multiplexed human viral pathogens reagent