In this task, you are given a paragraph (from a research paper) and your task is to classify each sentence of the paragraph (assume n sentence) into the following categories: Background, Purpose, Method, Finding/Contribution, or Other. Return the output in this format: 1 - category of sentence 1, 2 - category of sentence 2, ..., n - category of sentence n; where each number indicates the order of the sentence. The categories can be identified using the following questions: 
 Background: - Why is this problem important? - What relevant works have been done before? - What did the previous works miss? - What are the high-level research questions? - How might this help other researchers?
 Purpose: - What specific things do the researchers want to do? - What specific knowledge do the researchers want to know? - What specific hypothesis do the researchers want to test?
 Method: - How did the researchers do it or find it out? - What are the procedures and steps of this research?
 Finding/Contribution: - What did the researchers find out? Did the proposed methods work? - Did things behave as the researchers expected?
Other: put every text fragment that does not fit into any of the categories above here. Put a sentence here if its not in English, is not part of the article, contains numbers and dates, is a caption of a figure or a table, is a formatting error, or you are not sure about it.

Input: Consider Input: Viral channel forming proteins (VCPs) have been discovered in the late 70s and are found in many viruses to date. Usually they are small and have to assemble to form channels which depolarize the lipid membrane of the host cells. Structural information is just about to emerge for just some of them. Thus, computational methods play a pivotal role in generating plausible structures which can be used in the drug development process. In this review the accumulation of structural data is introduced from a historical perspective. Computational performances and their predictive power are reported guided by biological questions such as the assembly, mechanism of function and drug-protein interaction of VCPs. An outlook of how coarse grained simulations can contribute to yet unexplored issues of these proteins is given. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

Output: 1 - background, 2 - background, 3 - background, 4 - background, 5 - method, 6 - method, 7 - finding, 8 - finding, 9 - finding


Input: Consider Input: A capsid protein of porcine circovirus 2 (PCV 2) serves as a diagnostic antigen for the detection of PCV 2-associated disease known as a postweaning multisystemic wasting syndrome (PMWS). In this report, a bacterial expression system was developed for the expression and purification of the full-length PCV 2 capsid (Cap) protein from a codon-optimized cap gene. Replacement of rare arginine codons located at the 5 end of the cap reading frame with codons optimal for E. coli was found to overcome the poor expression of the viral protein in the prokaryotic system. The Cap protein was purified to greater than 95% homogeneity by using a single cation-exchange chromatography at a yield of 10 mg per litre of bacterial culture. Despite the failure of the E. coli-expressed Cap protein to self-assemble into virus-like particles (VLPs), the immunization of mice with recombinant Cap yielded antibodies with the same specificity as those raised against native PCV 2 virions. In addition, the antigenic properties of the purified Cap protein were employed in a subunit-based indirect ELISA to monitor the levels of PCV 2 specific antibodies in piglets originating from a herd which was experiencing PCV 2 infection. These results pave the way for a straightforward large-scale production of the recombinant PCV 2 capsid protein and its use as a diagnostic antigen or a PCV 2 subunit vaccine.

Output: 1 - background, 2 - method, 3 - finding, 4 - method, 5 - finding, 6 - finding, 7 - finding, 8 - finding


Input: Consider Input: The AsiaFluCap Simulator is freely available software (www.cdprg.org) which can be used by policy makers, policy advisors, donors and other stakeholders involved in preparedness for providing evidence based and illustrative information on health care resource capacities during future pandemics. The tool can inform both preparedness plans and simulation exercises and can help increase the general understanding of dynamics in resource capacities during a pandemic. The combination of a mathematical model with multiple resources and the linkage to GIS for creating maps makes the tool unique compared to other available software.


Output: 1 - finding, 2 - finding, 3 - finding, 4 - finding