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]: One word can be used to best describe the theme of each day that has transpired since the onset of the COVID-19 pandemic: uncertainty. Urologic surgical residents around the nation are faced with the unknown when they walk through the doors of the hospital each day. While experience has taught us navigational strategies to handle these circumstances, the COVID-19 pandemic has undoubtedly brought new meaning to this concept. Priorities have quickly transitioned from closely following our previously developed academic curriculum and enhancing our surgical skills, to limiting physician-patient encounters and ensuring the safety and health of our team. Normalcy within our professional and personal lives is completely lost. Yet, as health care providers, we must and will forge on. We review the techniques and guidelines that our institution has implemented in order to allow us to do just that. We believe that our leadership, both at an enterprise and program level, has taken the necessary and early measures to prepare our residency cohort for the uncertain future and challenges posed by COVID-19. This plan emphasizes social and professional distancing, allows residents and faculty to stay current on academic topics, and provides reasonable opportunities for personal wellness, all while emphasizing safety and rationalization. Most importantly, however, we have continued to provide our patients with exceptional urologic care at 2 adult hospitals and 1 children's hospital in the Greater Akron, Ohio area. While we understand each residency program has a unique curriculum with a different number of residents that cover a range of different hospitals, we hope to provide insight into our personal experience. We believe that this method of organization allows a residency program to continue to grow professionally, socially, academically, and personally during the uncertainty that is COVID-19. This framework could serve as an immediate action plan for future pandemics and natural disasters, especially for residency programs that cover multiple hospitals and health care settings.
[EX A]: Editorial Cleveland Clinic Akron General Urology Residency Program's COVID-19 Experience

[EX Q]: Background: Since 2010, outbreaks of genotype 2 (G2) porcine epidemic diarrhea virus (PEDV) have caused high mortality in neonatal piglets and have had devastating impacts on the swine industry in many countries. A reliable serological assay for evaluating the PEDV-specific humoral and mucosal immune response is important for disease survey, monitoring the efficacy of immunization, and designing strategies for the prevention and control of PED. Two PEDV spike (S) glycoprotein-based indirect enzyme-linked immunosorbent assays (ELISAs) were developed using G2b PEDV-Pintung 52 (PEDV-PT) trimeric full-length S and truncated S 1-501 proteins derived from the human embryonic kidney (HEK)-293 cell expression system. The truncated S 1-501 protein was selected from a superior expressed stable cell line. The sensitivity and specificity of these two ELISAs were compared to immunostaining of G2b PEDV-PT infected cells and to a commercial nucleocapsid (N)-based indirect ELISA kit using a panel of PEDV negative and hyperimmune sera. The commercial N-based ELISA exhibited a sensitivity of 37%, a specificity of 100%, and a fair agreement (kappa = 0.37) with the immunostaining result. In comparison, the full-length S-based ELISA showed a sensitivity of 97.8%, a specificity of 94%, and an almost perfect agreement (kappa = 0.90) with the immunostaining result. Interestingly, the S 1-501 -based ELISA had even higher sensitivity of 98.9% and specificity of 99.1%, and an almost perfect agreement (kappa = 0.97) with the immunostaining result. A fair agreement (kappa< 0.4) was seen between the commercial N-based ELISA and either of our S-based ELISAs. However, the results of the full-length S-based ELISA shared an almost perfect agreement (kappa = 0.92) with that of S 1-501 -based ELISA. (Continued on next page)
[EX A]: Development and comparison of enzyme- linked immunosorbent assays based on recombinant trimeric full-length and truncated spike proteins for detecting antibodies against porcine epidemic diarrhea virus

[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