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: Autophagy and autophagy-related processes are fundamentally important in human health and disease. These processes are viewed primarily as cellular degradative pathways that recycle macromolecules and dysfunctional or redundant organelles into amino acids, sugars and lipids, especially during starvation. However, the ubiquitin-like autophagy proteins and other components of the autophagic machinery additionally participate in cellular reprogramming. We highlight these non-autophagic roles of autophagy proteins with the aim of drawing attention to this growing, but unexplored, research topic. We focus on the non-autophagic functions of autophagy proteins in cell survival and apoptosis, modulation of cellular traffic, protein secretion, cell signalling, transcription, translation and membrane reorganization.

A: review review Non-autophagic roles of autophagy-related proteins
****
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: The coronavirus disease 2019 pandemic poses an enormous challenge to healthcare systems in affected communities. Older patients and those with pre-existing medical conditions have been identified as populations at risk of a severe disease course. It remains unclear at this point to what extent chronic liver diseases should be considered as risk factors, due to a shortage of appropriate studies. However, patients with advanced liver disease and those after liver transplantation represent vulnerable patient cohorts with an increased risk of infection and/or a severe course of COVID-19. In addition, the current pandemic requires unusual allocation of healthcare resources which may negatively impact the care of patients with chronic liver disease that continue to require medical attention. Thus, the challenge hepatologists are facing is to promote telemedicine in the outpatient setting, prioritise outpatient contacts, avoid nosocomial dissemination of the virus to patients and healthcare providers, and at the same time maintain standard care for patients who require immediate medical attention.

A:
Care of patients with liver disease during the COVID-19 pandemic: EASL-ESCMID position paper
****