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.

[EX Q]: Citation Imperiale MJ, Casadevall A. 2015. The importance of virology at a time of great need and great jeopardy. mBio 6(2):e00236-15.
[EX A]: 1 - background, 2 - background, 3 - other

[EX Q]: Quantification of physiologic defensin concentrations in vivo is complex, as defensins are present at high local concentrations within specific cell types or upon release from cells into confined anatomical niches (e.g., crypts of the small intestine) but can become diluted in extracellular fluids. For the myeloid -defensins, Daher et al. estimated ~3 mM (10 mg/ml) HNPs in neutrophils, with even higher local concentrations in the azurophilic granules in which they are stored 1 . For the enteric -defensins, Ayabe et al. estimated concentrations of ≥3.5 mM (15-100 mg/ml) in the crypt lumen, the site of Paneth cell degranulation 31 . These concentrations are likely similar in the human small intestine, where HD5 expression exceeds that of HD6 by 6-fold 32 . In healthy patients, epithelial lining fluid of the lung contains 31-79 nM HNP1-3, nasal fluid contains ~2.7 M HNP1-3, saliva contains 0.3-3 M HNP1-3, and vaginal secretions contain ~1.5 M HNPs and 0.3-14 M HD5 23; 33; 34; 35; 36; 37; 38 . For the -defensins, 5-10 nM HBD2 has been measured in nasal fluid 37; 39 . However, in certain disease states defensin levels can be highly elevated. For example, 57 M to 2.4 mM concentrations of HNP1 have been found in epithelial lining fluid of cystic fibrosis patients 36 . Overall, the concentrations of defensins present in vivo are generally within the range that is needed for direct antiviral activity by -defensins and generally below the concentrations required for direct antiviral activity by -defensins. Wilson et al. 
[EX A]: 1 - finding, 2 - finding, 3 - finding, 4 - finding, 5 - finding, 6 - finding, 7 - finding, 8 - finding, 9 - finding, 10 - finding, 11 - finding, 12 - finding, 13 - finding, 14 - finding, 15 - finding, 16 - finding, 17 - finding, 18 - finding, 19 - other

[EX Q]: Geo-social interaction patterns Geo-social scale Location-based social network Agent-based epidemic models Social network analysis Infectious disease transmission and control High performance computing A B S T R A C T Cities play an important role in fostering and amplifying the transmission of airborne diseases (e.g., influenza) because of dense human contacts. Before an outbreak of airborne diseases within a city, how to determine an appropriate containment area for effective vaccination strategies is unknown. This research treats airborne disease spreads as geo-social interaction patterns, because viruses transmit among different groups of people over geographical locations through human interactions and population movement. Previous research argued that an appropriate scale identified through human geo-social interaction patterns can provide great potential for effective vaccination. However, little work has been done to examine the effectiveness of such vaccination at large scales (e.g., city) that are characterized by spatially heterogeneous population distribution and movement. This article therefore aims to understand the impact of geo-social interaction patterns on effective vaccination in the urbanized area of Portland, Oregon. To achieve this goal, we simulate influenza transmission on a large-scale location-based social network to 1) identify human geo-social interaction patterns for designing effective vaccination strategies, and 2) and evaluate the efficacy of different vaccination strategies according to the identified geo-social patterns. The simulation results illustrate the effectiveness of vaccination strategies based on geosocial interaction patterns in containing the epidemic outbreak at the source. This research can provide evidence to inform public health approaches to determine effective scales in the design of disease control strategies.
[EX A]:
1 - background, 2 - background, 3 - background, 4 - background, 5 - background, 6 - background, 7 - background, 8 - purpose, 9 - method, 10 - method, 11 - finding, 12 - finding