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.
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Question: This paper combines two advances to detect MERS-CoV, the causative agent of Middle East Respiratory Syndrome, that have emerged over the past few years from the new field of "synthetic biology". Both are based on an older concept, where molecular beacons are used as the downstream detection of viral RNA in biological mixtures followed by reverse transcription PCR amplification. The first advance exploits the artificially expanded genetic information systems (AEGIS). AEGIS adds nucleotides to the four found in standard DNA and RNA (xNA); AEGIS nucleotides pair orthogonally to the A:T and G:C pairs. Placing AEGIS components in the stems of molecular beacons is shown to lower noise by preventing unwanted stem invasion by adventitious natural xNA. This should improve the signal-to-noise ratio of molecular beacons operating in complex biological mixtures. The second advance introduces a nicking enzyme that allows a single target molecule to activate more than one beacon, allowing "signal amplification". Combining these technologies in primers with components of a self-avoiding molecular recognition system (SAMRS), we detect 50 copies of MERS-CoV RNA in a multiplexed respiratory virus panel by generating fluorescence signal visible to human eye and/or camera.

Answer: Standard and AEGIS nicking molecular beacons detect amplicons from the Middle East respiratory syndrome coronavirus


Question: Introduction: Accurate and timely prediction for endemic infectious diseases is vital for public health agencies to plan and carry out any control methods at an early stage of disease outbreaks. Climatic variables has been identified as important predictors in models for infectious disease forecasts. Various approaches have been proposed in the literature to produce accurate and timely predictions and potentially improve public health response. Methods: We assessed how the machine learning LASSO method may be useful in providing useful forecasts for different pathogens in countries with different climates. Separate LASSO models were constructed for different disease/country/forecast window with different model complexity by including different sets of predictors to assess the importance of different predictors under various conditions. Results: There was a more apparent cyclicity for both climatic variables and incidence in regions further away from the equator. For most diseases, predictions made beyond 4 weeks ahead were increasingly discrepant from the actual scenario. Prediction models were more accurate in capturing the outbreak but less sensitive to predict the outbreak size. In different situations, climatic variables have different levels of importance in prediction accuracy. Conclusions: For LASSO models used for prediction, including different sets of predictors has varying effect in different situations. Short term predictions generally perform better than longer term predictions, suggesting public health agencies may need the capacity to respond at short-notice to early warnings.

Answer: The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison


Question: Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect virulencerelated genes in novel agents, as we show here for the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020.

Answer:
Interpretable detection of novel human viruses from genome sequencing data