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]: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious 24 disease localized to China, Japan, and Korea that is characterized by severe hemorrhage and a 25 high fatality rate. Currently, no specific vaccine or treatment has been approved for this 26 disease. To develop a therapeutic agent for SFTS, we isolated antibodies from a phage-27 displayed antibody library that was constructed from a patient who recovered from SFTS 28 virus (SFTSV) infection. One antibody, designated as Ab10, was reactive to the Gn envelope 29 glycoprotein of SFTSV and protected host cells and A129 mice from infection in both in 30 vitro and in vivo experiments. Notably, Ab10 protected 80% of mice, even when injected 5 31 days after inoculation with a lethal dose of SFTSV. Using cross-linker assisted mass 32 spectrometry and alanine scanning, we located the non-linear epitope of Ab10 on the Gn 33 glycoprotein domain II and an unstructured stem region, suggesting that Ab10 may inhibit a 34 conformational alteration that is critical for cell membrane fusion between the virus and host 35 cell. Ab10 reacted to recombinant Gn glycoprotein in Gangwon/Korea/2012, HB28, and SD4 36 strains. Additionally, based on its epitope, we predict that Ab10 binds the Gn glycoprotein in 37 247 of 272 reported SFTSV isolates previously reported. Together, these data suggest that 38 Ab10 has potential to be developed into a therapeutic agent that could protect against more 39 than 90% of reported SFTSV isolates. 40 41
[A]: An anti-Gn glycoprotein antibody from a convalescent patient potently 2 inhibits the infection of severe fever with thrombocytopenia syndrome virus


[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.
[A]: Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models


[Q]: Human T-cell leukemia virus type 1 (HTLV-1) causes adult T-cell leukemia (ATL) and chronic inflammatory diseases. HTLV-1 bZIP factor (HBZ) is transcribed as an antisense transcript of the HTLV-1 provirus. Among the HTLV-1-encoded viral genes, HBZ is the only gene that is constitutively expressed in all ATL cases. Recent studies have demonstrated that HBZ plays an essential role in oncogenesis by regulating viral transcription and modulating multiple host factors, as well as cellular signaling pathways, that contribute to the development and continued growth of cancer. In this article, I summarize the current knowledge of the oncogenic function of HBZ in cell proliferation, apoptosis, T-cell differentiation, immune escape, and HTLV-1 pathogenesis.
[A]:
viruses The Role of HBZ in HTLV-1-Induced Oncogenesis