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.

Input: Consider Input: 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.

Output: Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models


Input: Consider Input: Autophagy is a key innate immune response to intracellular parasites that promotes their delivery to degradative lysosomes following detection in the cytosol or within damaged vacuoles. Like Listeria and Shigella, which use specific mechanisms to avoid autophagic detection and capture, the bacterial pathogen Francisella tularensis proliferates within the cytosol of macrophages without demonstrable control by autophagy. To examine how Francisella evades autophagy, we screened a library of F. tularensis subsp. tularensis Schu S4 HimarFT transposon mutants in GFP-LC3-expressing murine macrophages by microscopy for clones localised within autophagic vacuoles after phagosomal escape. Eleven clones showed autophagic capture at six hours postinfection, whose HimarFT insertions clustered to four genetic loci involved in lipopolysaccharidic and capsular O-antigen biosynthesis. Consistent with the HimarFT mutants, in-frame deletion mutants of two representative loci, FTT1236 and FTT1448c (manC), lacking both LPS and capsular O-antigen, underwent phagosomal escape but were cleared from the host cytosol. Unlike wild type Francisella, the O-antigen deletion mutants were ubiquitinated, and recruited the autophagy adaptor p62/SQSTM1 and LC3 prior to cytosolic clearance. Autophagy-deficient macrophages partially supported replication of both mutants, indicating that O-antigen-lacking Francisella are controlled by autophagy. These data demonstrate the intracellular protective role of this bacterial surface polysaccharide against autophagy.

Output: The Francisella O-antigen mediates survival in the macrophage cytosol via autophagy avoidance NIH Public Access


Input: Consider Input: autoimmune arthritis. In the present study, we provide compelling experimental in vivo evidence in support of our hypothesis. Using the SCHOOL approach and the SARS-CoV fusion peptide sequence, we rationally designed a novel immunomodulatory peptide that targets TCR. We showed that this peptide ameliorates collagen-induced arthritis in DBA/1J mice and protects against bone and cartilage damage. Incorporation of the peptide into self-assembling lipopeptide nanoparticles that mimic native human high density lipoproteins significantly increases peptide dosage efficacy. Together, our data further confirm that viral immune evasion strategies that target MIRRs can be transferred to therapeutic strategies that require similar functionalities.


Output: SARS Coronavirus Fusion Peptide- Derived Sequence Suppresses Collagen-Induced Arthritis in DBA/1J Mice OPEN