In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

Input: Consider Input: Paragraph: Only a month after the outbreak of pneumonia caused by 2019-nCoV, more than forty-thousand people were infected. This put enormous pressure on the Chinese government, medical healthcare provider, and the general public, but also made the international community deeply nervous. On the 25th day after the outbreak, the Chinese government implemented strict traffic restrictions on the area where the 2019-nCoV had originateddHubei province, whose capital city is Wuhan. Ten days later, the rate of increase of cases in Hubei showed a significant difference (p ¼ 0.0001) compared with the total rate of increase in other provinces of China. These preliminary data suggest the effectiveness of a traffic restriction policy for this pandemic thus far. At the same time, solid financial support and improved research ability, along with network communication technology, also greatly facilitated the application of epidemic prevention measures. These measures were motivated by the need to provide effective treatment of patients, and involved consultation with three major groups in policy formulationdpublic health experts, the government, and the general public. It was also aided by media and information technology, as well as international cooperation. This experience will provide China and other countries with valuable lessons for quickly coordinating and coping with future public health emergencies. 
 Title: Lessons learned from the 2019-nCoV epidemic on prevention of future infectious diseases

Output: False


Input: Consider Input: Paragraph: Background: Interaction between immune system and Chandipura virus (CHPV) during different stages of its life cycle remain poorly understood. The exact route of virus entry into the blood and CNS invasion has not been clearly defined. The present study was undertaken to assess the population in PBMC that supports the growth of virus and to detect active virus replication in PBMC as well as its subsets. Methods: PBMC subsets viz.: CD3 + , CD14 + , CD19 + , CD56 + cells were separated and infected with CHPV. The infected cells were then assessed for transcription (N gene primer) and replication (NP gene primer) of CHPV by PCR. The supernatant collected from infected cells were titrated in Baby Hamster Kidney (BHK) cells to assess virus release. The cytokine and chemokine expression was quantified by flow cytometry. Results: Amplification of N and NP gene was detected in CD14 + (monocyte) and CD19 + (B cell), significant increase in virus titre was also observed in these subsets. It was observed that, although the levels of IL-6 and IL-10 were elevated in CD14 + cells as compared to CD19 + cells, the differences were not significant. However the levels of TNFα and IL-8 were significantly elevated in CD14 + cells than in CD19 + cells. The levels of chemokine (CXCL9, CCL5, CCL2, CXCL10) were significantly elevated in CHPV infected PBMC as compared to uninfected cells. CCL2 and CXCL9 were significantly increased in CHPV infected CD14 + cells as compared to CD19 + cells. Conclusion: CD14 + and CD19 + cells support active replication of CHPV. High viral load was detected in CD14 + cells infected with CHPV hence it might be the primary target cells for active replication of CHPV. An elevated levels of cytokines and chemokines observed in CD14 + cells may help in predicting the pathogenecity of CHPV and possible entry into the central nervous system. 
 Title: Monocytes and B cells support active replication of Chandipura virus

Output: True


Input: Consider Input: Paragraph: Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progress in this area, reconstructing networks with stochastic dynamical processes from limited time series remains to be an outstanding problem. Here we develop a framework based on compressed sensing to reconstruct complex networks on which stochastic spreading dynamics take place. We apply the methodology to a large number of model and real networks, finding that a full reconstruction of inhomogeneous interactions can be achieved from small amounts of polarized (binary) data, a virtue of compressed sensing. Further, we demonstrate that a hidden source that triggers the spreading process but is externally inaccessible can be ascertained and located with high confidence in the absence of direct routes of propagation from it. Our approach thus establishes a paradigm for tracing and controlling epidemic invasion and information diffusion in complex networked systems. ½S i ðt 2 Þ þ S i ðt 6 Þ=2 and hS i ðt 2 þ 1Þi ¼ ½S i ðt 12 Þ þ S i ðt 14 Þ=2 based on the remaining strings marked in different colours (see Methods for more details). CST can be used to reconstruct the neighbouring vector X of node 2 from Y and U from Y ¼ U . X. 
 Title: ARTICLE Reconstructing propagation networks with natural diversity and identifying hidden sources


Output: False