TASK DEFINITION: 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.
PROBLEM: Early innate and cell-intrinsic responses are essential to protect host cells against pathogens. In turn, viruses have developed sophisticated mechanisms to establish productive infections, counteracting the host innate immune responses. Increasing evidence indicates that these antiviral factors may have a dual role by directly inhibiting viral replication, as well as by sensing and transmitting signals to induce antiviral cytokines. Recent studies have pointed at new, unappreciated mechanisms of viral evasion of host innate protective responses including manipulating the host ubiquitin system. Viral inhibition of antiviral factors by ubiquitin-dependent degradation is emerging as critical evasion mechanism of the antiviral response. In addition, recent studies have uncovered new mechanisms by which viral encoded proteins inhibit ubiquitin and ubiquitin-like modification of host proteins involved innate immune signaling pathways. Here we discuss recent findings and novel strategies that viruses have developed to counteract these early innate antiviral defenses. Early events in viral infection include attachment to the host cell membrane and entry, release of the viral RNA, DNA, and protein complexes into the cytoplasm of the cell and early recognition by the host innate immune system. At each of these steps, host cells have developed mechanisms to limit virus infection. In turn, viruses have rapidly evolved to counteract host defenses by different mechanisms. Many recent studies have pointed at the importance of cell type and host specific expression of antiviral factors as critical for

SOLUTION: Viral evasion mechanisms of early antiviral responses involving regulation of ubiquitin pathways NIH Public Access

PROBLEM: The lengthy duration of multidrug therapy needed to cure tuberculosis (TB) poses significant challenges for global control of the disease. Moreover, chronic inflammation associated with TB leads to pulmonary damage that can remain even after successful cure. Thus, there is a great need for the development of effective shorter drug regimens to improve clinical outcome and strengthen TB control. Host-directed therapy (HDT) is emerging as a novel adjunctive strategy to enhance the efficacy and shorten the duration of TB treatment. Previously, we showed that the administration of CC-3052, a phosphodiesterase-4 inhibitor (PDE4i), reduced the host inflammatory response during Mycobacterium tuberculosis (Mtb) infection and improved the antimicrobial efficacy of isoniazid (INH) in both the mouse and rabbit models. In the present study, we evaluated the pharmacokinetics and explored the mechanism underlying the efficacy of a more potent PDE4i, CC-11050, as adjunct to INH treatment in a mouse model of pulmonary Mtb infection. Genome-wide lung transcriptome analysis confirmed the dampening of inflammation and associated network genes that we previously reported with CC-3052. Consistent with the reduction in inflammation, a significant improvement in Mtb control and pathology was observed in the lungs of mice treated with CC-11050 plus INH, compared to INH alone. This important confirmatory study will be used to help design upcoming human clinical trials with CC-11050 as an HDT for TB treatment.

SOLUTION: Pharmacologic inhibition of host Phosphodiesterase-4 improves isoniazid-Mediated clearance of Mycobacterium tuberculosis

PROBLEM: h i g h l i g h t s A modified community network model with heterogeneity among communities is proposed. A mathematical epidemic model for each community is presented based on this network. Study the effect of the location of initial infection node on epidemic spreading. Study the impact of the heterogeneity among communities on epidemic spreading. a b s t r a c t A large number of real world networks exhibit community structure, and different communities may often possess heterogeneity. In this paper, considering the heterogeneity among communities, we construct a new community network model in which the communities show significant differences in average degree. Based on this heterogeneous community network, we propose a novel mathematical epidemic model for each community and study the epidemic dynamics in this network model. We find that the location of the initial infection node only affects the spreading velocity and barely influences the epidemic prevalence. And the epidemic threshold of entire network decreases with the increase of heterogeneity among communities. Moreover, the epidemic prevalence increases with the increase of heterogeneity around the epidemic threshold, while the converse situation holds when the infection rate is much greater than the epidemic threshold. (B. Song). example, the severe acute respiratory syndrome (SARS) in 2003, the H1N1 influenza A virus in 2009, the H7N9 avian influenza virus in 2013, the Ebola virus in 2014 and the Middle East respiratory syndrome coronavirus (MERS-CoV) in 2015. The problem of epidemic spreading has gained great attention over the years and people want to predict the epidemic spread trend and take effective public health measures with limited vaccine supply. As the topology properties of networks have a profound impact on the dynamics of epidemic spreading, it is necessary to consider the effect of community structure on epidemic spreading. So far, a lot of results on epidemic dynamics in community networks have been obtained [12, 14, 16, 29, 30] . To simulate the real network, many different kinds of community network models were constructed based on classical networks, and some individual behavior characteristics in real networks (such as random walk, long-range jump and awareness) were also taken into account. Liu and Hu studied the SIS (susceptible-infected-susceptible) dynamics on a random community network model with probability p (q) of intra-(inter-)

SOLUTION:
Modeling and analysis of epidemic spreading on community networks with heterogeneity