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".

[Q]: Paragraph: Despite the significant role of seasonal influenza vaccination in preventing and minimizing the serious complications of influenza infection in type 2 diabetes mellitus (T2DM) patients, unsatisfactory compliance still exists for vaccination. Study objectives were to explore the vaccination status and determinants in T2DM patients in southwestern Saudi Arabia. A cross-sectional study on a representative sample of T2DM patients in Abha city, southwestern Saudi Arabia, was conducted. Data for sociodemographic characteristics, clinical criteria, vaccination status, vaccination motivators and barriers and seasonal influenza knowledge were collected. Out of 353 T2DM patients included in the study, seasonal influenza vaccination coverage was 61% in year 2017. A significant factors associated with non-vaccination were; poor influenza and its vaccine knowledge (OR = 4.31, 95% CI: 2.73-6.80), illiteracy (OR = 1.93, 95% CI: 1.11-3.37), and more than 10 years disease duration (OR = 2.07, 95% CI: 1.11-3.87). Presence of family history of DM and ischemic heart comorbidity minimized the possibility of non-vaccination (OR = 0.54 and 0.28 respectively). Healthcare givers' advice was the most reported vaccination motivator (84.7%) while; fear of vaccine side effects was the most stated barrier (73%). In conclusion, influenza vaccination rate among T2DM in the present study is less than the recommended level. Continuous primary health care center-based educational programs should be implemented to aware and encourage influenza vaccination among T2DM patients. 
 Title: Influenza Vaccination in Type 2 Diabetes Patients: Coverage Status and Its Determinants in Southwestern Saudi Arabia
[A]: True


[Q]: 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
[A]: False


[Q]: Paragraph: KEYWORDS Respiratory viruses Laboratory diagnosis Molecular methods Polymerase chain reaction Molecular tests for the detection of respiratory viruses are more sensitive and can detect more viruses than the traditional methods of culture and antigen detection. There are now several molecular assays available, cleared by the Food and Drug Administration, which differ with respect to the viruses detected, instrumentation, throughput, hands-on time, the need for separate nucleic acid extraction, and sensitivity for certain groups of viruses. Issues associated with molecular tests for respiratory viruses include: possible falsenegative results due to sequence variants; the inability of many assays to discriminate rhinoviruses from enteroviruses; the ability to detect viral nucleic acids in asymptomatic individuals; and the increased prevalence of coinfections. 
 Title: Molecular Detection of Respiratory Viruses
[A]:
False