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".

Ex Input:
Paragraph: Human adenoviral (HAd) vectors have demonstrated great potential as vaccine vectors. Preclinical and clinical studies have demonstrated the feasibility of vector design, robust antigen expression and protective immunity using this system. However, clinical use of adenoviral vectors for vaccine purposes is anticipated to be limited by vector immunity that is either preexisting or develops rapidly following the first inoculation with adenoviral vectors. Vector immunity inactivates the vector particles and rapidly removes the transduced cells, thereby limiting the duration of transgene expression. Due to strong vector immunity, subsequent use of the same vector is usually less efficient. In order to circumvent this limitation, nonhuman adenoviral vectors have been proposed as alternative vectors. In addition to eluding HAd immunity, these vectors possess most of the attractive features of HAd vectors. Several replication-competent or replication-defective nonhuman adenoviral vectors have been developed and investigated for their potential as vaccine delivery vectors. Here, we review recent advances in the design and characterization of various nonhuman adenoviral vectors, and discuss their potential applications for human and animal vaccination. 
 Title: Development of nonhuman adenoviruses as vaccine vectors

Ex Output:
False


Ex Input:
Paragraph: 1. Predicting infectious disease dynamics is a central challenge in disease ecology. Models 36 that can assess which individuals are most at risk of being exposed to a pathogen not only 37 provide valuable insights into disease transmission and dynamics but can also guide 38 management interventions. Constructing such models for wild animal populations, 39 however, is particularly challenging; often only serological data is available on a subset 40 of individuals and non-linear relationships between variables are common. 41 42 2. Here we take advantage of the latest advances in statistical machine learning to construct 43 pathogen-risk models that automatically incorporate complex non-linear relationships 44 with minimal statistical assumptions from ecological data with missing values. Our 45 approach compares multiple machine learning algorithms in a unified environment to 46 find the model with the best predictive performance and uses game theory to better 47 interpret results. We apply this framework on two major pathogens that infect African 48 lions: canine distemper virus (CDV) and feline parvovirus. 49 50 3. Our modelling approach provided enhanced predictive performance compared to more 51 traditional approaches, as well as new insights into disease risks in a wild population. We 52 were able to efficiently capture and visualise strong non-linear patterns, as well as model 53 complex interactions between variables in shaping exposure risk from CDV and feline 54 parvovirus. For example, we found that lions were more likely to be exposed to CDV at a 55 young age but only in low rainfall years. 56 57 . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/569012 doi: bioRxiv preprint 3 4. When combined with our data calibration approach, our framework helped us to answer 58 questions about risk of pathogen exposure which are difficult to address with previous 59 methods. Our framework not only has the potential to aid in predicting disease risk in 60 animal populations, but also can be used to build robust predictive models suitable for 61 other ecological applications such as modelling species distribution or diversity patterns. 
 Title: How to make more from exposure data? An integrated machine Author Contributions Data Accessibility Statement Running Title Machine learning and pathogen exposure risk

Ex Output:
True


Ex Input:
Paragraph: Previously developed Asn-Gly-Arg (NGR) peptide-modified multifunctional poly(ethyleneimine)-poly(ethylene glycol) (PEI-PEG)-based nanoparticles (TPIC) have been considered to be promising carriers for the co-delivery of DNA and doxorubicin (DOX). As a continued effort, the aim of the present study was to further evaluate the interaction between TPIC and human umbilical vein endothelial cells (HUVEC) to better understand the cellular entry mechanism. In the present investigation, experiments relevant to co-localization, endocytosis inhibitors and factors influencing the internalization were performed. Without any treatment, there was no co-localization between aminopeptidase N/CD13 (APN/CD13) and caveolin 1 (CAV1). However, co-localization between CD13 and CAV1 was observed when cells were incubated with an anti-CD13 antibody or TPIC. As compared with antibody treatment, TPIC accelerated the speed and enhanced the degree of co-localization. TPIC entered HUVEC not only together with CD13 but also together with CAV1. However, this internalization was not dependent on the enzyme activity of CD13 but could be inhibited by methyl-β-eyclodextfin (MβCD), further identifying the involvement of caveolae-mediated endocytosis (CvME). This conclusion was also verified by endocytosis inhibitor experiments. 
 Title: A preliminary study on the interaction between Asn-Gly-Arg (NGR)-modified multifunctional nanoparticles and vascular epithelial cells-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Ex Output:

False