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.

[EX Q]: Estimating temporal changes in a target population from phylogenetic or count data is an important problem in ecology and epidemiology. Reliable estimates can provide key insights into the climatic and biological drivers influencing the diversity or structure of that population and evidence hypotheses concerning its future growth or decline. In infectious disease applications, the individuals infected across an epidemic form the target population. The renewal model estimates the effective reproduction number, R, of the epidemic from counts of its observed cases. The skyline model infers the effective population size, N, underlying a phylogeny of sequences sampled from that epidemic. Practically, R measures ongoing epidemic growth while N informs on historical caseload. While both models solve distinct problems, the reliability of their estimates depends on p-dimensional piecewise-constant functions. If p is misspecified, the model might underfit significant changes or overfit noise and promote a spurious understanding of the epidemic, which might misguide intervention policies or misinform forecasts. Surprisingly, no transparent yet principled approach for optimising p exists. Usually, p is heuristically set, or obscurely controlled via complex algorithms. We present a computable and interpretable p-selection method based on the minimum description length (MDL) formalism of information theory. Unlike many standard model selection techniques, MDL accounts for the additional statistical complexity induced by how parameters interact. As a result, our method optimises p so that R and N estimates properly adapt to the available data. It also outperforms comparable Akaike and Bayesian information criteria on several classification problems. Our approach requires some knowledge of the parameter space and exposes the similarities between renewal and skyline models.
[EX A]: Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models

[EX Q]: Combinations of porcine respiratory coronavirus (PRCV) and either of two swine influenza viruses (H1N1 or H3N2) were administered intranasally and by aerosol to six-to eight-week-old specific pathogen:free pigs. The clinical responses, gross respiratory lesions and growth performances of these pigs were studied and compared with those of single (PRCV, H1N1 or H3N2) and mock-infected animals. PRCV infection caused fever, growth retardation and lung lesions, but no respiratory symptoms. Infection with swine influenza viruses caused rather similar, mild symptoms of disease, with H1N1 infection being the least severe. Combined infections with influenza viruses and PRCV did not appear to enhance the pathogenicity of these viruses. Furthermore, viruses were isolated more frequently from tissues and nasal swabs taken from 'single' than 'dual' infected animals, suggesting a possible in vivo interference between replication of PRCV and swine influenza virus.
[EX A]: Pathogenicity of concurrent infection of pigs with porcine respiratory coronavirus and swine influenza virus

[EX Q]: Recent approaches using nanoparticles engineered for immune regulation have yielded promising results in preclinical models of disease. The number of nanoparticle therapies is growing, fueled by innovations in nanotechnology and advances in understanding of the underlying pathogenesis of immune-mediated diseases. In particular, recent mechanistic insight into the ways in which nanoparticles interact with the mononuclear phagocyte system and impact its function during homeostasis and inflammation have highlighted the potential of nanoparticle-based therapies for controlling severe inflammation while concurrently restoring peripheral immune tolerance in autoimmune disease. Here we review recent advances in nanoparticle-based approaches aimed at immune-modulation, and discuss these in the context of concepts in polymeric nanoparticle development, including particle modification, delivery and the factors associated with successful clinical deployment.
[EX A]:
Harnessing nanoparticles for immune modulation