instruction:
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".
question:
Paragraph: Assembly of virus capsids and surface proteins must be regulated to ensure that the resulting complex is an infectious virion. Here we examine assembly of virus capsids, focusing on hepatitis B virus and bacteriophage MS2, and formation of glycoproteins in the alphaviruses. These systems are structurally and biochemically well-characterized and are simplest-case paradigms of self-assembly. Published data suggest that capsid and glycoprotein assembly is subject to allosteric regulation, that is, regulation at the level of conformational change. The hypothesis that allostery is a common theme in viruses suggests that deregulation of capsid and glycoprotein assembly by small molecule effectors will be an attractive antiviral strategy, as has been demonstrated with hepatitis B virus. 
 Title: Virus assembly, allostery, and antivirals
answer:
False


question:
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
answer:
True


question:
Paragraph: Human dendritic cell-specific intercellular adhesion molecule-1 grabbing nonintegrin, DC-SIGN, and the sinusoidal endothelial cell receptor DC-SIGNR or L-SIGN, are closely related sugar-binding receptors. DC-SIGN acts both as a pathogen-binding endocytic receptor and as a cell adhesion molecule, while DC-SIGNR has only the pathogen-binding function. In addition to differences in the sugar-binding properties of the carbohydrate-recognition domains in the two receptors, there are sequence differences in the adjacent neck domains, which are coiled-coil tetramerization domains comprised largely of 23-amino acid repeat units. A series of model polypeptides consisting of uniform repeat units have been characterized by gel filtration, differential scanning calorimetry and circular dichroism. The results demonstrate that two features characterize repeat units which form more stable tetramers: a leucine reside in the first position of the heptad pattern of hydrophobic residues that pack on the inside of the coiled coil and an arginine residue on the surface of the coiled coil that forms a salt bridge with a glutamic acid residue in the same polypeptide chain. In DC-SIGNR from all primates, very stable repeat units predominate, so the carbohydrate-recognition domains must be held relatively closely together. In contrast, stable repeat units are found only near the membrane in DC-SIGN. The presence of residues that disrupt tetramer formation in repeat units near the carbohydrate-recognition domains of DC-SIGN would allow these domains to splay further apart. Thus, the neck domains of DC-SIGN and DC-SIGNR can contribute to the different functions of these receptors by presenting the sugar-binding sites in different contexts. The spacing between sugar-binding sites in receptors of the innate immune system contributes to the way that these receptors distinguish sugars on the surface of pathogens from those on host cells. This work describes how subtle differences in closely related receptors can change the way that sugarbinding sites are held together, helping to explain why they interact differently with viruses that display similar types of sugars. 
 Title: Oligomerization domains in the glycan-binding receptors DC-SIGN and DC-SIGNR: Sequence variation and stability differences
answer:

False