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:
The performance of the new ePlex Respiratory Pathogen (RP) panel (GenMark Diagnostics) for the simultaneous detection of 19 viruses (influenza A virus; influenza A H1 virus; influenza A 2009 H1 virus; influenza A H3 virus; influenza B virus; adenovirus; coronaviruses [HKU1, OC43, NL63, and 229E]; human rhinovirus/enterovirus; human metapneumovirus; parainfluenza viruses 1, 2, 3, and 4; and respiratory syncytial virus [RSV] [RSV subtype A and RSV subtype B] ) and 2 bacteria (Mycoplasma pneumoniae and Chlamydia pneumoniae) was evaluated. Prospectively and retrospectively collected nasopharyngeal swab (NPS) specimens (n ϭ 2,908) were evaluated by using the ePlex RP panel, with the bioMérieux/BioFire FilmArray Respiratory Panel (BioFire RP) as the comparator method. Discordance analysis was performed by using target-specific PCRs and bidirectional sequencing. The reproducibility of the assay was evaluated by using reproducibility panels comprised of 6 pathogens. The overall agreement between the ePlex RP and BioFire RP results was Ͼ95% for all targets. Positive percent agreement with the BioFire RP result for viruses ranged from 85.1% (95% confidence interval [CI], 80.2% to 88.9%) to 95.1% (95% CI, 89.0% to 97.9%), while negative percent agreement values ranged from 99.5% (95% CI, 99.1% to 99.7%) to 99.8% (95% CI, 99.5% to 99.9%). Additional testing of discordant targets (12%; 349/2,908) confirmed the results of ePlex RP for 38% (131/349) of samples tested. Reproducibility was 100% for all targets tested, with the exception of adenovirus, for which reproducibilities were 91.6% at low virus concentrations and 100% at moderate virus concentrations. The ePlex RP panel offers a new, rapid, and sensitive "sample-to-answer" multiplex panel for the detection of the most common viral and bacterial respiratory pathogens.

Ex Output:
Multicenter Evaluation of the ePlex Respiratory Pathogen Panel for the Detection of Viral and Bacterial Respiratory Tract Pathogens in Nasopharyngeal Swabs


Ex Input:
Interferon-inducible transmembrane proteins 1, 2, and 3 (IFITM1, 2, and 3) are recently identified viral restriction factors that inhibit infection mediated by the influenza A virus (IAV) hemagglutinin (HA) protein. Here we show that IFITM proteins restricted infection mediated by the entry glycoproteins (GP 1,2 ) of Marburg and Ebola filoviruses (MARV, EBOV). Consistent with these observations, interferon-b specifically restricted filovirus and IAV entry processes. IFITM proteins also inhibited replication of infectious MARV and EBOV. We observed distinct patterns of IFITM-mediated restriction: compared with IAV, the entry processes of MARV and EBOV were less restricted by IFITM3, but more restricted by IFITM1. Moreover, murine Ifitm5 and 6 did not restrict IAV, but efficiently inhibited filovirus entry. We further demonstrate that replication of infectious SARS coronavirus (SARS-CoV) and entry mediated by the SARS-CoV spike (S) protein are restricted by IFITM proteins. The profile of IFITM-mediated restriction of SARS-CoV was more similar to that of filoviruses than to IAV. Trypsin treatment of receptor-associated SARS-CoV pseudovirions, which bypasses their dependence on lysosomal cathepsin L, also bypassed IFITM-mediated restriction. However, IFITM proteins did not reduce cellular cathepsin activity or limit access of virions to acidic intracellular compartments. Our data indicate that IFITM-mediated restriction is localized to a late stage in the endocytic pathway. They further show that IFITM proteins differentially restrict the entry of a broad range of enveloped viruses, and modulate cellular tropism independently of viral receptor expression. This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.

Ex Output:
Distinct Patterns of IFITM-Mediated Restriction of Filoviruses, SARS Coronavirus, and Influenza A Virus


Ex Input:
An actual indoor contact network can be affected by many factors such as the age, gender, occupation, personal habits, social status, geographical location, while many of these factors may correlate with each other. Hence, it is impossible to mathematically describe, accurately and completely, a real social contact network. To create a representative synthetic contact network and to simulate a possible infectious disease outbreak based on the network, we adopt a few simplifications. We separated population and indoor environments into representative groups and assumed that individuals in the same group share similar location visiting behaviors, and all of the indoor environments in the same group share similar characteristics such as the number of occupants, occupant density, and ventilation rate. Internal variations within each group can be simulated by assuming the possible distributions for different parameters. Visiting time The complex patterns of individuals arriving at and leaving a location are not considered. We simplified the scenario by dividing each indoor environment into different sub-visiting locations that represent different time durations for visiting each indoor environment. Thus, several locations may represent the same indoor environment at a different time. Therefore this approach differentiates individuals who visit the same indoor environment at different times. We also assumed that individuals who choose to visit a location (an indoor environment for a fixed duration) come at the same time and leave at the same time. Hence, the visiting time of all possible visitors to a given location is the same. We assumed that all of the individuals in a population group have the same location visiting schedule on weekdays. During a normal weekday, an individual may visit several locations in different location groups with a fixed order. To simulate the variations in an individual's daily choices, we assumed that for each step of an individual's schedule, he or she has a collection of favorite locations. Random contact is not considered in this model.

Ex Output:

Supplementary Information for Building ventilation as an effective disease intervention strategy in a dense indoor contact network in an ideal city