In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Ex Input:
Background: Nonadditivity in fitness effects from two or more mutations, termed epistasis, can result in compensation of deleterious mutations or negation of beneficial mutations. Recent evidence shows the importance of epistasis in individual evolutionary pathways. However, an unresolved question in molecular evolution is how often and how significantly fitness effects change in alternative genetic backgrounds. Results: To answer this question, we quantified the effects of all single mutations and double mutations between all positions in the IgG-binding domain of protein G (GB1). By observing the first two steps of all possible evolutionary pathways using this fitness profile, we were able to characterize the extent and magnitude of pairwise epistasis throughout an entire protein molecule. Furthermore, we developed a novel approach to quantitatively determine the effects of single mutations on structural stability (DDG U ). This enabled determination of the importance of stability effects in functional epistasis. Conclusions: Our results illustrate common biophysical mechanisms for occurrences of positive and negative epistasis. Our results show pervasive positive epistasis within a conformationally dynamic network of residues. The stability analysis shows that significant negative epistasis, which is more common than positive epistasis, mostly occurs between combinations of destabilizing mutations. Furthermore, we show that although significant positive epistasis is rare, many deleterious mutations are beneficial in at least one alternative mutational background. The distribution of conditionally beneficial mutations throughout the domain demonstrates that the functional portion of sequence space can be significantly expanded by epistasis.

Ex Output:
Article A Comprehensive Biophysical Description of Pairwise Epistasis throughout an Entire Protein Domain


Ex Input:
The present study describes the development of SYBR Green based real-time polymerase chain reaction (real-time PCR) for detection and quantitation of canine parvovirus type 2 (CPV 2) in faecal samples of dogs. In this assay, the primers were designed and custom-synthesized based on nucleotide sequence of VP2 gene of CPV 2. A standard curve was plotted using 10-fold serial dilution of standard plasmid DNA and Ct value. The standard curve was found to be linear over a 10 −7 dilution. The real-time PCR results were expressed as the number of DNA copies of CPV 2 per mg of faecal samples and showed range of 1.0 × 10 3 to 7.0 × 10 9 copies of viral DNA per mg of stool samples. The analytical sensitivity of the SYBR Green based real-time PCR was shown to be equivalent to 10 copies. Faecal samples (47) from dogs suspected of CPV 2 infection were analyzed by real-time PCR, haemagglutination (HA) assay and by a conventional PCR and 24, 20 and 22 samples were found positive for CPV 2, respectively. Comparison between the results of three different assays revealed that real-time PCR is more sensitive than HA and conventional PCR and allow the detection of low titers of CPV 2 in infected dogs.

Ex Output:
Development of a SYBR Green based real-time PCR assay for detection and quantitation of canine parvovirus in faecal samples


Ex Input:
A number of features were included in the final models, including article counts, indicator counts and slopes. In Table 2 -1, the ten features with highest Gini importance are listed for Model 0 → 1 and Model 1 → 0, predicting Y 1 i jt and training on all available weeks. There is substantial overlap between the lists for the two models. For both models, the article count and the mortality, outbreak worsening and novelty indicators were among the most important features. The mortality indicator describes whether or not fatalities were reported during the week, the outbreak worsening indicator captures mentions of increasing cases or worsening conditions, and the novelty indicator measures whether the disease is described as new or unfamiliar. The finding that the novelty indicator was among the most important features is consistent with previous research that has shown that outbreaks of novel diseases are more likely to have associated social response (Fast et al, 2015; McGrath, 1991) . Table 2-1: The ten features with highest Gini importance for Model 0 → 1 and Model 1 → 0 Model 0 → 1 Model 1 → 0 Social Response Indicator EWMA as a Percentage of UCL In this section, we present results for the 30 countries used for model specification and tuning and compare with the overall results in order to access the extent to which tuning and model specification may have affected results for these countries. We experimented with different model specifications and combinations of features on a subset of data consisting of 30 countries, prior to applying our models to the entire collection of countries. Table 2 -2 presents the model performance metrics for both the entire collection of 72 countries and for the subset of 30 countries used for model specification and tuning. Model performance was slightly diminished on the entire collection of countries compared with the 30 countries used for model specification and tuning. In part, the poorer performance can be explained by the fact that the average number of articles per week in the entire collection of countries was lower than in the 30 countries used for model specification -0.14 articles per week versus 3.50 articles per week. We have shown that in general our models perform best on country-disease pairs that receive substantial news coverage.

Ex Output:

Predicting Social Response to Infectious Disease Outbreaks from Internet-based News Streams Online Resource 2: Supplemental Model Results Article Count without Duplicates 3 Week Slope of EWMA Article Count with Duplicates Disease Name Mortality Indicator Article Count without Duplicates 3 Week Slope of Mortality Indicator Article Count with Duplicates Outbreak Worsening Indicator Mortality Indicator 3 Week Slope of Outbreak Worsening Indicator Outbreak Worsening Indicator Population Fear Indicator Novelty Indicator Novelty Indicator Officials Fear Indicator 2 Results for the 30 Countries Used for Model Specification and Tuning