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.
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Question: June 19, 2017) from four Manatee County sites (Fig. 1a) . To avoid cross contamination of 33 mosquito viromes, each year eggs from each site were hatched independently in distilled 34 water, reared to adulthood, speciated and then frozen. Female abdomens were pooled (N= 35 20/pool) separately for the four collection sites for a total of eight individual pools. Total RNA 36 was extracted using the AllPrep DNA/RNA Mini Kit (Qiagen), and rRNA depleted with the 37 NEBNext rRNA Depletion Kit (New England BioLabs). The NEBNext Ultra II Directional RNA 38 Library Prep Kit (New England BioLabs) was used to prepare shotgun metagenomics libraries. 39 Reverse-transcribed RNA libraries were sequenced using a HiSeq 3000 (Illumina) instrument 40 in 2x101 run mode. The data were deposited into the NCBI Sequence Read Archive and 41 Biosample archive under BioProject PRJNA547758. 42 43 Initial assembly and metavirome analysis 44 BBduk was used to trim adaptor 45 sequences and remove contaminants. Ae. aegypti sequences were removed using BBsplit 46 author/funder. All rights reserved. No reuse allowed without permission. : bioRxiv preprint 9 assembly with Spades in meta mode [8] and classification using Diamond [9] for a complete 71 DENV4 genome. Read-mapping with Bowtie [10] revealed incorrect bases near the 3' end, 72 which were manually corrected. The genome was annotated using the Genome Annotation 73 Transfer Utility [16] from the Virus Pathogen Database and Analysis Resource (ViPR) [17]. 74 75 Phylogenetic and Molecular Clock analyses 76 Two hundred thirty-four DENV4 genome sequences from GenBank (Table S1) were aligned 77 using MAFFT version 7.407 [18] with the L-INS-I method [19]. IQ-TREE software [20] was 78 used to evaluate phylogenetic signal in the genomes by likelihood mapping [21] and to infer 79 maximum likelihood (ML) phylogeny based on the best-fit model according to the Bayesian 80 Information Criterion (BIC) [20, 22]. Statistical robustness for internal branching order was 81 assessed by Ultrafast Bootstrap (BB) Approximation (2,000 replicates), and strong statistical 82 support was defined as BB>90% [23]. 83 84 To estimate when DENV4 entered Florida, we used 145 strains including all isolates from the 85 Americas, related Asian and African isolates, and randomly reduced oversampled Brazilian 86 isolates. The strains in this dataset were not recombinant, as assessed by scanning the 87 alignments for possible recombination points using the RDP, GENECONV, MaxChi, 88 CHIMAERA, and 3Seq algorithms implemented in the RDP4 software [24]. Correlation between root-to-tip genetic 90 divergence and date of sampling was conducted [25] to assess clock signal before Bayesian 91 phylodynamic analysis. Time-scaled trees were reconstructed using the Bayesian 92 phylodynamic inference framework in BEAST v.1.8.4 [26,27]. Markov Chain Monte Carlo 93 (MCMC) samplers were run until 200/250 million generations to ensure Markov chain mixing, 94 author/funder. All rights reserved. No reuse allowed without permission.

Answer: 1 - method, 2 - method, 3 - method, 4 - method, 5 - method, 6 - method, 7 - method, 8 - method, 9 - method, 10 - method, 11 - method, 12 - other, 13 - method, 14 - other, 15 - other, 16 - method, 17 - finding, 18 - finding, 19 - method, 20 - method, 21 - method, 22 - method, 23 - finding, 24 - method, 25 - method, 26 - method, 27 - method, 28 - finding, 29 - finding, 30 - finding, 31 - finding, 32 - finding, 33 - finding, 34 - finding, 35 - other, 36 - other


Question: Objective: In cases of community acquired pneumonia (CAP), it has been known that blood cultures have low yields and rarely affect clinical outcomes. Despite many studies predicting the likelihood of bacteremia in CAP patients, those results have been rarely implemented in clinical practice, and use of blood culture in CAP is still increasing. This study evaluated impact of implementing a previously derived and validated bacteremia prediction rule. Methods: In this registry-based before and after study, we used piecewise regression analysis to compare the blood culture rate before and after implementation of the prediction rule. We also compared 30-day mortality, emergency department (ED) length of stay, time-interval to initial antibiotics after ED arrival, and any changes to the antibiotics regimen as results of the blood cultures. In subgroup analysis, we compared two groups (with or without the use of the prediction rule) after implementation period, using propensity score matching. Results: Following the implementation, the blood culture rate declined from 85.5% to 78.1% (P = 0.003) without significant changes in 30-day mortality and antibiotics regimen. The interval to initial antibiotics (231 min vs. 221 min, P = 0.362) and length of stay (1019 min vs. 954 min, P = 0.354) were not significantly changed. In subgroup analysis, the group that use the prediction rule showed 25 min faster antibiotics initiation (P = 0.002) and 48 min shorter length of stay (P = 0.007) than the group that did not use the rule. Conclusion: Implementation of the bacteremia prediction rule in CAP patients reduced the blood culture rate without affecting the 30-day mortality and antibiotics regimen.

Answer: 1 - purpose, 2 - purpose, 3 - purpose, 4 - purpose, 5 - purpose, 6 - purpose, 7 - method, 8 - method, 9 - method, 10 - method, 11 - method, 12 - method, 13 - method, 14 - finding, 15 - finding, 16 - finding, 17 - finding, 18 - finding


Question: Conclusion: VIRsiRNApred is the first algorithm for predicting inhibition efficacy of viral siRNAs which is developed using experimentally verified viral siRNAs. We hope this algorithm would be useful in predicting highly potent viral siRNA to aid siRNA based antiviral therapeutics development. The web server is freely available at crdd.

Answer:
1 - finding, 2 - finding, 3 - finding, 4 - finding


