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: Porcine deltacoronavirus (PDCoV) is a newly discovered coronavirus, which belongs to the family Coronaviridae. It causes watery diarrhea, vomiting, and dehydration in newborn piglets. A sensitive RT-PCR method is urgently required to detect PDCoV infection. In this study, we developed and evaluated a conventional RT-PCR assay and a SYBR green-based real-time RT-PCR assay that targeted the PDCoV gene. Both assays are specific and have the same limit of detection at 2 × 10 1 copies of RNA molecules per reaction. Eighty-four clinical samples were subjected to both conventional RT-PCR and real-time RT-PCR, and the same positive rate (41.7%) was achieved, which was much higher than the positive rate (26.2%) using a previously described onestep RT-PCR technique. In summary, a conventional RT-PCR technique was successfully established for the detection of PDCoV with the same detection limit as a SYBR green-based real-time RT-PCR assay.
Example Output: Research Article

Example Input: This article employs a state-of-the-art panel threshold model by allowing for regime intercepts, in order to shed new light on the asymmetric/ nonlinear effects of local and global sentiments on expected industry stock returns among 11 Asian countries during the period from 1996 to 2010. Empirical evidence demonstrates that once the regime intercept is included, the asymmetric effects of global sentiment on oil & gas, financials, and health care industry returns become less under optimism, as compared with under pessimism. More critically, the positive (negative) impact of global sentiment above (under) the threshold turns significant, indicating that global optimism leads industry returns to be overvalued, while pessimism leads them to be undervalued. For local market sentiment, our results support that higher local sentiment enhances the returns of basic materials, telecommunications, and utilities industries. The empirical results confirm that the nexus of industry returns and investor sentiments is subject to change between different sentimental intervals.
Example Output: Asymmetric effects of investor sentiment on industry stock returns: Panel data evidence

Example Input: Motivation: Accurately determining the atomic structure of proteins represents a fundamental problem in the field of structural bioinformatics. A solution would be significant as protein structure information could be utilized in the medical field, e.g. in the development of vaccines for new viruses. This paper focuses on predicting the protein structure based on 3D images of the proteins captured through cryogenic electron microscopes (cryo-EM). A fully automated computationally efficient protein structure prediction method would be particularly beneficial in the field of cryo-EM as the technology allows researchers to photograph multiple large protein complexes in a single study, which means that a fast prediction method could allow for a high throughput of derived protein structures. We present a deep learning approach, DeepTracer, for predicting locations of the backbone atoms, secondary structure elements, and the amino acid types. In order to connect the predicted amino acids into chains, we applied a modified traveling salesman algorithm. Results: We trained our deep learning model on experimental cryo-EM density maps and tested it on a set of 50 density maps. We found that our new approach predicted protein structures with an average RMSD value of 1.18 and a coverage of 87.5%. Furthermore, we detected secondary structure information for 87.2% of amino acids correctly. We also showed preliminarily that 25.2% of amino acid types could be predicted directly from the 3D cryo-EM density map, considering 20 different types in total. Finally, we noted that the prediction runtime of DeepTracer is significantly improved compared to other methods. It predicts a large protein complex structure of more than 30,000 amino acids in only 2 hours. Availability: The repository of this project will be published. There are two main approaches to predict protein structures: homology modeling and de novo structure prediction. They differ in such that homology modeling requires structural knowledge about a homologous protein for its prediction while de novo methods do not. This limits the
Example Output:
DeepTracer: Predicting Backbone Atomic Structure from High Resolution Cryo-EM Den- sity Maps of Protein Complexes XXXXXXX Received on XXXXX; revised on XXXXX; accepted on XXXXX