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]: We have previously reported that the apathogenic Tula hantavirus induces apoptosis in Vero E6 epithelial cells. To assess the molecular mechanisms behind the induced apoptosis we studied the effects of hantavirus infection on cellular signaling pathways which promote cell survival. We previously also observed that the Tula virus-induced cell death process is augmented by external TNF-α. Since TNF-α is involved in the pathogenesis of hantavirus-caused hemorrhagic fever with renal syndrome (HFRS) we investigated its effects on HFRS-causing hantavirus-infected cells. We studied both apathogenic (Tula and Topografov) and pathogenic (Puumala and Seoul) hantaviruses for their ability to regulate cellular signaling pathways and observed a direct virusmediated down-regulation of external signal-regulated kinases 1 and 2 (ERK1/2) survival pathway activity, which was dramatically enhanced by TNF-α. The fold of ERK1/2 inhibition correlated with viral replication efficiencies, which varied drastically between the hantaviruses studied. We demonstrate that in the presence of a cytokine TNF-α, which is increased in HFRS patients, hantaviruses are capable of inactivating proteins that promote cell survival (ERK1/ 2). These results imply that hantavirus-infected epithelial cell barrier functions might be compromised in diseased individuals and could at least partially explain the mechanisms of renal dysfunction and the resulting proteinuria seen in HFRS patients.
[A]: Virology Journal Hantaviruses and TNF-alpha act synergistically to induce ERK1/2 inactivation in Vero E6 cells


[Q]: Attenuated viral vaccines can be generated by targeting essential pathogenicity factors. We report here the rational design of an attenuated recombinant coronavirus vaccine based on a deletion in the coding sequence of the nonstructural protein 1 (nsp1). In cell culture, nsp1 of mouse hepatitis virus (MHV), like its SARS-coronavirus homolog, strongly reduced cellular gene expression. The effect of nsp1 on MHV replication in vitro and in vivo was analyzed using a recombinant MHV encoding a deletion in the nsp1-coding sequence. The recombinant MHV nsp1 mutant grew normally in tissue culture, but was severely attenuated in vivo. Replication and spread of the nsp1 mutant virus was restored almost to wild-type levels in type I interferon (IFN) receptor-deficient mice, indicating that nsp1 interferes efficiently with the type I IFN system. Importantly, replication of nsp1 mutant virus in professional antigen-presenting cells such as conventional dendritic cells and macrophages, and induction of type I IFN in plasmacytoid dendritic cells, was not impaired. Furthermore, even low doses of nsp1 mutant MHV elicited potent cytotoxic T cell responses and protected mice against homologous and heterologous virus challenge. Taken together, the presented attenuation strategy provides a paradigm for the development of highly efficient coronavirus vaccines.
[A]: Coronavirus Non-Structural Protein 1 Is a Major Pathogenicity Factor: Implications for the Rational Design of Coronavirus Vaccines


[Q]: Background: For real time surveillance, detection of abnormal disease patterns is based on a difference between patterns observed, and those predicted by models of historical data. The usefulness of outbreak detection strategies depends on their specificity; the false alarm rate affects the interpretation of alarms. We evaluate the specificity of five traditional models: autoregressive, Serfling, trimmed seasonal, wavelet-based, and generalized linear. We apply each to 12 years of emergency department visits for respiratory infection syndromes at a pediatric hospital, finding that the specificity of the five models was almost always a non-constant function of the day of the week, month, and year of the study (p < 0.05). We develop an outbreak detection method, called the expectation-variance model, based on generalized additive modeling to achieve a constant specificity by accounting for not only the expected number of visits, but also the variance of the number of visits. The expectation-variance model achieves constant specificity on all three time scales, as well as earlier detection and improved sensitivity compared to traditional methods in most circumstances. Modeling the variance of visit patterns enables real-time detection with known, constant specificity at all times. With constant specificity, public health practitioners can better interpret the alarms and better evaluate the cost-effectiveness of surveillance systems.
[A]:
BMC Medical Informatics and Decision Making Automated real time constant-specificity surveillance for disease outbreaks