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.

Example Input: The emerging novel human betacoronavirus 2c EMC/2012 (HCoV-EMC) was recently isolated from patients with severe pneumonia and renal failure and was associated with an unexplained high crude fatality rate of 56%. We performed a cell line susceptibility study with 28 cell lines. HCoV-EMC was found to infect the human respiratory tract ( polarized airway epithelium cell line Calu-3, embryonic fibroblast cell line HFL, and lung adenocarcinoma cell line A549), kidney (embryonic kidney cell line HEK), intestinal tract (colorectal adenocarcinoma cell line Caco-2), liver cells (hepatocellular carcinoma cell line Huh-7), and histiocytes (malignant histiocytoma cell line His-1), as evident by detection of high or increasing viral load in culture supernatants, detection of viral nucleoprotein expression by immunostaining, and/or detection of cytopathic effects. Although an infected human neuronal cell line (NT2) and infected monocyte and T lymphocyte cell lines (THP-1, U937, and H9) had increased viral loads, their relatively lower viral production corroborated with absent nucleoprotein expression and cytopathic effects. This range of human tissue tropism is broader than that for all other HCoVs, including SARS coronavirus, HCoV-OC43, HCoV-HKU1, HCoV-229E, and HCoV-NL63, which may explain the high mortality associated with this disease. A recent cell line susceptibility study showed that HCoV-EMC can infect primate, porcine, and bat cells and therefore may jump interspecies barriers. We found that HCoV-EMC can also infect civet lung fibroblast and rabbit kidney cell lines. These findings have important implications for the diagnosis, pathogenesis, and transmission of HCoV-EMC.
Example Output: Differential Cell Line Susceptibility to the Emerging Novel Human Betacoronavirus 2c EMC/2012: Implications for Disease Pathogenesis and Clinical Manifestation

Example Input: Introduction: Accurate and timely prediction for endemic infectious diseases is vital for public health agencies to plan and carry out any control methods at an early stage of disease outbreaks. Climatic variables has been identified as important predictors in models for infectious disease forecasts. Various approaches have been proposed in the literature to produce accurate and timely predictions and potentially improve public health response. Methods: We assessed how the machine learning LASSO method may be useful in providing useful forecasts for different pathogens in countries with different climates. Separate LASSO models were constructed for different disease/country/forecast window with different model complexity by including different sets of predictors to assess the importance of different predictors under various conditions. Results: There was a more apparent cyclicity for both climatic variables and incidence in regions further away from the equator. For most diseases, predictions made beyond 4 weeks ahead were increasingly discrepant from the actual scenario. Prediction models were more accurate in capturing the outbreak but less sensitive to predict the outbreak size. In different situations, climatic variables have different levels of importance in prediction accuracy. Conclusions: For LASSO models used for prediction, including different sets of predictors has varying effect in different situations. Short term predictions generally perform better than longer term predictions, suggesting public health agencies may need the capacity to respond at short-notice to early warnings.
Example Output: The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison

Example Input: Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI (''sequence-based ultrarapid pathogen identification''), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7-500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times.
Example Output:
A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples