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: The aim in this study was to assess the effectiveness of a quaternary ammonium chloride (QAC) surfactant in reducing surface staphylococcal contamination in a routinely operating medical ward occupied by patients who had tested positive for methicillin-resistant Staphylococcus aureus (MRSA). The QAC being tested is an antibacterial film that is sprayed onto a surface and can remain active for up to 8 h. A field experimental study was designed with the QAC plus daily hypochlorite cleaning as the experimental group and hypochlorite cleaning alone as the control group. The method of swabbing on moistened surfaces was used for sampling. It was found that 83% and 77% of the bedside surfaces of MRSA-positive and MRSA-negative patients respectively were contaminated with staphylococci at 08:00 hours, and that the staphylococcal concentrations increased by 80% at 1200 h over a 4-hour period with routine ward and clinical activities. Irrespective of the MRSA status of the patients, high-touch surfaces around the bed-units within the studied medical ward were heavily contaminated (ranged 1 to 276 cfu/cm 2 amongst the sites with OPEN ACCESS Int. J. Environ. Res. Public Health 2015, 12 3027 positive culture) with staphylococcal bacteria including MRSA, despite the implementation of daily hypochlorite wiping. However, the contamination rate dropped significantly from 78% to 11% after the application of the QAC polymer. In the experimental group, the mean staphylococcal concentration of bedside surfaces was significantly (p < 0.0001) reduced from 4.4 ± 8.7 cfu/cm 2 at 08:00 hours to 0.07 ± 0.26 cfu/cm 2 at 12:00 hours by the QAC polymer. The results of this study support the view that, in addition to hypochlorite wiping, the tested QAC surfactant is a potential environmental decontamination strategy for preventing the transmission of clinically important pathogens in medical wards.

Output: 1 - purpose, 2 - method, 3 - method, 4 - method, 5 - finding, 6 - finding, 7 - finding, 8 - finding, 9 - other, 10 - other, 11 - finding, 12 - finding, 13 - finding, 14 - finding, 15 - finding, 16 - finding, 17 - finding


Input: Consider Input: 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.

Output: 1 - background, 2 - background, 3 - background, 4 - background, 5 - background, 6 - background, 7 - background, 8 - purpose, 9 - method, 10 - method, 11 - finding, 12 - finding


Input: Consider Input: The role of respiratory viruses in the pathogenesis of Kawasaki disease (KD) remains controversial. In this study we showed that 8.8 % of KD patients had documented respiratory viral infections. Patients with concomitant viral infections had a higher frequency of coronary artery dilatations and were significantly more often diagnosed with incomplete KD. The presence of a concomitant viral infection should not exclude the diagnosis of KD.


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