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]: . The role of interferon in spontaneous cell-mediated cytotoxicity in pigs. Vet. Microbiol., I i : 69 -77. Specific release of StCr and the production of interferon (IFN) increased in parallel in a spontaneous cell-mediated cytotoxicity (SCMC) assay in which uninfected PK-15 cells or PK-15 cells persistently infected with transmissible gastroenteritis virus (PK-15-TGE cells) were used as targets, and peripheral blood lymphocytes (PBL) from a young adult pig were used as effector cells. Higher levels of both specific S~Cr release and IFN were obtained in the assays containing PK-15-TGE cells. Co-cultivation of PBL from newborn piglets with PK-15-TGE cells yielded similar levels of IFN to those produced by co-cultivation of adult PBL and PK-15-TGE cells, but lower levels of IFN were produced by co-cultivation with uninfected PK-15 cells. Pretreatment of adult PBL with IFN augmented their SCMC effector activity for both PK-15 and PK-15-TGE cells in the S~Cr release assay. Pretreatment of the PK-15-TGE target cells with IFN did not affect their release of S*Cr in the SCMC reaction, while IFN pretreatment of PK-15 targets protected them against SCMC. In a single cell cytotoxicity assay the effects of IFN pretreatment on the effector adult PBL and on the PK-15 and PK-15-TGE target cells were confirmed, and SCMC incompetent PBL from neonatal piglets were rendered cytotoxic by pretreatment with IFN. PBL from newborn piglets bound to either target cell with the same frequency as PBL from SCMC competent adult pigs, and IFN pretreatment of either effector or target cells had no effect on target-binding frequency.
[EX A]: THE ROLE OF INTERFERON IN SPONTANEOUS CELL-MEDIATED CYTOTOXICITY IN PIGS

[EX Q]: Coronaviridae and causes highly contagious respiratory disease in chickens. Innate immunity plays significant roles in host defense against IBV. Here, we explored the interaction between IBV and the host innate immune system. Severe histopathological lesions were observed in the tracheal mucosa at 3-5 days post inoculation (dpi) and in the kidney at 8 dpi, with heavy viral loads at 1-11 and 1-28 dpi, respectively. The expression of mRNAs encoding Toll-like receptor (TLR) 3 and TLR7 were upregulated at 3-8 dpi, and that of TIR-domain-containing adapter-inducing interferon (IFN) β (TRIF) was upregulated at 21 dpi in the trachea and kidney. Myeloid differentiation primary response protein 88 (MyD88) was upregulated in the trachea during early infection. Tumor necrosis factor receptor-associated factor (TRAF) 3 and TRAF6 were upregulated expression in both tissues. Moreover, melanoma differentiation-associated protein 5 (MDA5), laboratory of genetics and physiology 2 (LGP2), stimulator of IFN genes (STING), and mitochondrial antiviral signaling protein (MAVS), as well as TANK binding kinase 1 (TBK1), inhibitor of kappaB kinase (IKK) ԑ, IKKα, IKKβ, IFN regulatory factor (IRF) 7, nuclear factor of kappaB (NF-κB), IFN-α, IFN-β, various interleukins(ILs), and macrophage inflammatory protein-1β (MIP-1β) were significantly upregulated in the trachea and downregulated in the kidney. These results suggested that the TLR and MDA5 signaling pathways and innate immune cytokine were induced after IBV infection. Additionally, consistent responses to IBV infection were observed during early infection, with differential and complicated responses in the kidney.
[EX A]: Responses of the Toll-like receptor and melanoma differentiation- associated protein 5 signaling pathways to avian infectious bronchitis virus infection in chicks

[EX Q]: Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study's models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society.
[EX A]:
Predicting Infectious Disease Using Deep Learning and Big Data