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.

Example Input: Bovine respiratory disease (BRD) often occurs when active respiratory virus infections (BHV-1, etc.) impair resistance to Mannheimia haemolytica infection in the lower respiratory tract. The interactions that occur when the respiratory epithelium encounters these viral and bacterial pathogens are poorly understood. We used Agilent bovine gene microarray chips containing 44,000 transcripts to elucidate bovine bronchial epithelial cell (BBEC) responses following in vitro exposure to BHV-1 alone, M. haemolytica alone, or both BHV-1 and M. haemolytica. Microarray analysis revealed differential regulation (>2-fold) of 978 transcripts by BHV-1 alone, 2040 transcripts by M. haemolytica alone, and 2189 genes by BHV-1 and M. haemolytica in combination. M. haemolytica treatment produced significantly greater inductions (>10-fold) of several inflammation associated genes, such as CXCL2, IL-6, IL-1␣, e-selectin, and IL-8, than to BHV-1 alone. Functional analysis of the microarray data revealed a significant upregulation of genes involved in important biological processes such as inflammation (TNF-␣, IL-8, Tlr-2, IL-1, CXCL2, CSF2), vascular functions (VEGF, EDN2) and leukocyte migration (ICAM1, IL-16) during a co-infection with BHV-1 and M. haemolytica compared to either pathogen alone. This study provides evidence to support that lung epithelial cells are a source of mediators that may promote inflammatory changes observed during bovine respiratory disease.
Example Output: 1 - background, 2 - background, 3 - method, 4 - method, 5 - finding, 6 - finding, 7 - finding, 8 - finding, 9 - finding, 10 - finding, 11 - finding, 12 - finding, 13 - finding

Example Input: Over the past few decades, global metapopulation epidemic simulations built with worldwide air-transportation data have been the main tool for studying how epidemics spread from the origin to other parts of the world (e.g., for pandemic influenza, SARS, and Ebola). However, it remains unclear how disease epidemiology and the air-transportation network structure determine epidemic arrivals for different populations around the globe. Here, we fill this knowledge gap by developing and validating an analytical framework that requires only basic analytics from stochastic processes. We apply this framework retrospectively to the 2009 influenza pandemic and 2014 Ebola epidemic to show that key epidemic parameters could be robustly estimated in real-time from public data on local and global spread at very low computational cost. Our framework not only elucidates the dynamics underlying global spread of epidemics but also advances our capability in nowcasting and forecasting epidemics.
Example Output: 1 - background, 2 - background, 3 - background, 4 - purpose, 5 - method, 6 - finding

Example Input: Equivalently, for each detected case there are at least 1.8 (95% CI 0.5-3.4) undetected cases. If the model is correct, this is an upper bound on the detection frequency because (1) Singapore's detection is probably not 100% efficient. Singapore had as of 12 February 2020 eight
Example Output:
1 - finding, 2 - finding, 3 - finding, 4 - finding