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: Microbes have made a phenomenal contribution to the health and well-being of people throughout the world. In addition to producing many primary metabolites, such as amino acids, vitamins and nucleotides, they are capable of making secondary metabolites, which constitute half of the pharmaceuticals on the market today and provide agriculture with many essential products. This review centers on these beneficial secondary metabolites, the discovery of which goes back 80 years to the time when penicillin was discovered by Alexander Fleming.

Output: Microbial drug discovery: 80 years of progress


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: A novel coronavirus has been associated with a worldwide outbreak of atypical pneumonia referred to as Severe Acute Respiratory Syndrome (SARS-CoV). SARS-CoV nucleocapsid (N) protein has been cloned sequenced and expressed in Escherichia coli strain. Purified N protein was used to measure the SARS-CoV specific IgG antibodies from 16 SARS-CoV infected patients' sera and from 131 control subjects using ELISA assay. Specific antibody responses to the purified recombinant N protein after 10, 20, and 30 days of disease onset were observed in 13 of 16 (81.3%), 16 of 16 (100%) and 16 of 16 (100%) SARS patients sera, respectively. Comparison of detection results with a commercially available diagnostic kit coated with a mixture of SARS-CoV viral proteins showed 9 of 16 (56.3%), 13 of 16 (81.3%), and 15 of 16 (93.7%) positive responses, respectively. None of 131 control sera gave positive reaction in either assay. This data suggests that the N protein of SARS-CoV is immunodominant and this ELISA based test assay for detecting the SARS-CoV N antigen may hold a significant value for SARS diagnosis.


Output: Cloning, sequencing, expression, and purification of SARS-associated coronavirus nucleocapsid protein for serodiagnosis of SARS