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: Escherichia coli O157:H7, an important food-borne pathogen, has become a major public health concern worldwide. The aim of this study was to investigate the molecular epidemiologic feature of E. coli O157:H7 strains in China. 105 E. coli O157:H7 isolates were collected from various hosts and places over 9 years. A multilocus sequence typing scheme (MLST) was applied for bacteria genotyping and polymerase chain reaction (PCR) was used for virulence factor identification. Seven new MLST sequence types (STs), namely ST836, ST837, ST838, ST839, ST840, ST841, and ST842 were identified, which grouped into two lineages. Phylogenetic analysis suggested that the most two frequent STs in China, ST837 and ST836, may be the derivatives of E. coli O157:H7 Sakai or E. coli O157:H7 EDL933. Geographical diversity and host variety of E. coli O157:H7 were observed in China. In addition, the different distribution of tccp was detected. The data presented herein provide new insights into the molecular epidemiologic feature of E. coli O157:H7, and aid in the investigation of the transmission regularity and evolutionary mechanism of E. coli O157:H7.

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


Input: Consider Input: Purpose Influenza A viruses, human coronaviruses (hCoV) and human bocavirus (hBoV) are emerging respiratory viruses. This study investigated the association between influenza A viruses co-infection with hBoV and hCoV and severity and the sensitivity of a real-time polymerase chain reaction (RT-PCR) assay for identification of 15 coronaviruses. Methodology Published sequences for the 15 human coronaviruses were used to design a consensus PCR targeting the replicase open reading frame 1b. A previously published PCR targeting the NS1 Gene of all known human bocavirus strains was also utilized. A series of 217 samples from patients aged 37.7 (SD ± 30.4)] with seasonal influenza A viruses (SeasFluA) identified between 06/2011 and 06/2012 in NW England were tested for hCoV and hBoV using RT-PCR. Association between coinfection and disease outcome was assessed using logistic regression. Results The limit of detection of hCoV RT-PCR assay was 2 copies/µl of human coronavirus RNA template, a sensitivity comparable to a previously published SYBR green assay for human coronaviruses. A total of 12 hCoV and 17 hBoV were identified in the 217 influenza A positive samples. A higher proportion (61.5 %; 8/13) of SeasFluA/hBoV co-infections were identified in patients

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


Input: Consider Input: The global spread of Coronavirus Disease 2019 (COVID-19) is overwhelming many health-care systems. As a result, epidemiological models are being used to inform policy on how to effectively deal with this pandemic. We note that the majority of existing models do not take into account differences in the amount of interactions between individuals (i.e. the underlying human interaction network). Using network science we demonstrate how this network of interactions can be used to predict the spread of the virus and to inform policy on the most successful mitigation and suppression strategies. Although applicable to disease modelling in general, our results emphasize how network science can improve the predictive power of current COVID-19 epidemiological models. We provide commented source code for all our analyses so that they can easily be integrated into current and future epidemiological models.


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