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.

[Q]: Cut-off Cut-off Supplementary Figure 1 | Setting cut-off to identify links and hidden source. (a) Coarse-grained distribution of element values ln(1 − λ i )a ij times −1 in the reconstructed vector X of all nodes in a Newman-Watts small-world network (NW), where bin accumulation is implemented for the original distribution. The peak centered at zero corresponds to null connections and the other corresponds to actual links. A cut-off value can be set within the gap between the two peaks. (b) The values of ln(1 − λ i )a ij times -1 in the reconstructed vector X for Erdös-Rényi random networks (ER), Watts-Strogatz small-world networks (WS), NW and Barabási-Albert scale-free networks (BA), where green and yellow points correspond to existent links and null connections, respectively. All the networks share the same cut-off value of about 0.13. (c) Coarse-grained distribution of the structural variance σ in the NW network, where bin accumulation is exercised on the original distribution. The largest peak centered close zero corresponds to nodes of indirect neighbors of the hidden source and the large positive values of σ are associated with the immediate neighbors of the hidden source. A cut-off can be set within the largest gap. (d) The variance σ * i of element values in the reconstructed vector x of all nodes in the presence of a hidden source for ER, WS, NW and BA networks, where green and yellow points correspond to immediate neighbors and indirect neighbors of the hidden source, respectively. All the networks share the same cut-off values of about σ * i = 0.9. The network size in a,b is 200 and in c,d is 50, Θ = 0.25, ∆ = 0.45, λ i and δ i are uniformly distributed in the ranges (0.2, 0.4) and (0.4, 0.6), respectively. SIS dynamics are used for the illustration. Panels a and c correspond to Fig. 2a with nt = 0.4 and Fig. 5 in the main text, respectively.
[A]: 1 - background, 2 - background, 3 - background, 4 - background, 5 - background, 6 - background, 7 - background, 8 - background, 9 - background, 10 - background, 11 - background, 12 - background, 13 - finding, 14 - finding, 15 - finding, 16 - finding, 17 - finding, 18 - finding, 19 - finding, 20 - finding, 21 - finding, 22 - finding, 23 - finding, 24 - finding


[Q]: The dynamics of H5N1 influenza virus pathogenesis are multifaceted and can be seen as an emergent property that cannot be comprehended without looking at the system as a whole. In past years, most of the high-throughput studies on H5N1-host interactions have focused on the host transcriptomic response, at the cellular or the lung tissue level. These studies pointed out that the dynamics and magnitude of the innate immune response and immune cell infiltration is critical to H5N1 pathogenesis. However, viral-host interactions are multidimensional and advances in technologies are creating new possibilities to systematically measure additional levels of 'omic data (e.g. proteomic, metabolomic, and RNA profiling) at each temporal and spatial scale (from the single cell to the organism) of the host response. Natural host genetic variation represents another dimension of the host response that determines pathogenesis. Systems biology models of H5N1 disease aim at understanding and predicting pathogenesis through integration of these different dimensions by using intensive computational modeling. In this review, we describe the importance of 'omic studies for providing a more comprehensive view of infection and mathematical models that are being developed to integrate these data. This review provides a roadmap for what needs to be done in the future and what computational strategies should be used to build a global model of H5N1 pathogenesis. It is time for systems biology of H5N1 pathogenesis to take center stage as the field moves towards a more comprehensive view of virushost interactions.
[A]: 1 - background, 2 - background, 3 - background, 4 - background, 5 - background, 6 - background, 7 - background, 8 - purpose, 9 - finding, 10 - finding


[Q]: Methods:The non-randomized, controlled trial was conducted on 447 SARS patients treated synchronously with western conventional treatment (WM group, n = 171) alone and ICWM (ICWM group, n = 276). The changes of the cases with normal level ()95 % ) or abnormal level (~95 % ) SaOz were observed dynamically. Results: In the 3rd-14th day of the therapeutic course, the percentage of patients with normal SaC2 in the ICWM group was higher than that in the WM group (OR = 0. 5178, P--0. 0038), and this tendency was more evident in patients of the severe type (OR = 0.18, P = 0. 0001 ). However, the statistical significance of difference was only shown in patients for whom the ICWM treatment started in the early period after the onset (47 days after it, OR = 0. 3803, P = 0. 006), but not shown in those who received ICWM treatment later in the mid-late period of SARS (P~0.05).
[A]:
1 - method, 2 - method, 3 - finding, 4 - finding, 5 - finding, 6 - finding, 7 - finding, 8 - finding, 9 - finding, 10 - finding