Genome sequence analyses of the 2014 Ebola Virus (EBOV) isolates revealed a potential problem with the diagnostic assays currently in use; i.e., drifting genomic profiles of the virus may affect the sensitivity or even produce false-negative results. We evaluated signature erosion in ebolavirus molecular assays using an in silico approach and found frequent potential false-negative and false-positive results. We further empirically evaluated many EBOV assays, under real time PCR conditions using EBOV Kikwit (1995) and Makona (2014) RNA templates. These results revealed differences in performance between assays but were comparable between the old and new EBOV templates. Using a whole genome approach and a novel algorithm, termed BioVelocity, we identified new signatures that are unique to each of EBOV, Sudan virus (SUDV), and Reston virus (RESTV). Interestingly, many of the current assay signatures do not fall within these regions, indicating a potential drawback in the past assay design strategies. The new signatures identified in this study may be evaluated with real-time reverse transcription PCR (rRT-PCR) assay development and validation. In addition, we discuss regulatory implications and timely availability to impact a rapidly evolving outbreak using existing but perhaps less than optimal assays versus redesign these assays for addressing genomic changes.

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In a connected-vehicle environment, wireless subsecond data exchange connects vehicles, the infrastructure, and travelers’ mobile devices. These data have the promise to transform the geographic scope, precision, and latency of transportation system control; fulfillment of that promise could result in significant safety, mobility, and environmental benefits. However, the new data influx also has the potential to overburden legacy computational and communication systems. Although connected-vehicle technology can facilitate ubiquitous system coverage, the existing prediction methods, computational platforms, and data management methods are insufficient to process the data within a reasonable time frame for real-time predictions. An investigation of the ways in which advanced (big-data) analytics might be applied to realize the full potential of connected-vehicle technology is particularly relevant now as this technology evolves from research to deployment. This paper presents an approach combining big-data graph analytics with high-performance computing to predict traffic congestion by analyzing nearly 4 billion basic safety messages generated by the safety pilot model deployment conducted in 2012–2013. This paper provides an alternative approach for predicting congestion in 30.5-m segments anywhere on the network at 1-min intervals 30 to 60 min before actual congestion over a time window of 1 h. Despite sparseness of data, the proposed framework predicted highly congested locations 40% of the time. Severity of congestion was predicted with an accuracy of 77%. This combination of rapid computation and predictive accuracy may provide significant value in future real-time decision support systems that leverage connected-vehicle data.

22Background: Emerging and reemerging infectious diseases such as the novel Coronavirus 23 disease, COVID-19 and Ebola pose a significant threat to global society and test the public 24 health community's preparedness to rapidly respond to an outbreak with effective diagnostics 25 and therapeutics. Recent advances in next generation sequencing technologies enable rapid 26 generation of pathogen genome sequence data, within 24 hours of obtaining a sample in some 27 instances. With these data, one can quickly evaluate the effectiveness of existing diagnostics and 28 therapeutics using in silico approaches. The propensity of some viruses to rapidly accumulate 29 mutations can lead to the failure of molecular detection assays creating the need for redesigned 30 or newly designed assays. 31Results: Here we describe a bioinformatics system named BioLaboro to identify signature 32 regions in a given pathogen genome, design PCR assays targeting those regions, and then test the 33 PCR assays in silico to determine their sensitivity and specificity. We demonstrate BioLaboro 34 with two use cases: Bombali Ebolavirus (BOMV) and the novel Coronavirus 2019 (SARS-CoV-35 2). For the BOMV, we analyzed 30 currently available real-time reverse transcription-PCR 36 assays against the three available complete genome sequences of BOMV. Only two met our in 37 silico criteria for successful detection and neither had perfect matches to the primer/probe 38 sequences. We designed five new primer sets against BOMV signatures and all had true positive 39 hits to the three BOMV genomes and no false positive hits to any other sequence. Four assays 40 are closely clustered in the nucleoprotein gene and one is located in the glycoprotein gene. 41

Real-time reverse transcription polymerase chain reaction (RT-PCR) assays are the most widely used molecular tests for the detection of SARS-CoV-2 and diagnosis of COVID-19 in clinical samples. PCR assays target unique genomic RNA regions to identify SARS-CoV-2 with high sensitivity and specificity. In general, assay development incorporates the whole genome sequences available at design time to be inclusive of all target species and exclusive of near neighbors. However, rapid accumulation of mutations in viral genomes during sustained growth in the population can result in signature erosion and assay failures, creating situational blind spots during a pandemic. In this study, we analyzed the signatures of 43 PCR assays distributed across the genome against over 1.6 million SARS-CoV-2 sequences. We present evidence of significant signature erosion emerging in just two assays due to mutations, while adequate sequence identity was preserved in the other 41 assays. Failure of more than one assay against a given variant sequence was rare and mostly occurred in the two assays noted to have signature erosion. Assays tended to be designed in regions with statistically higher mutations rates. in silico analyses over time can provide insights into mutation trends and alert users to the emergence of novel variants that are present in the population at low proportions before they become dominant. Such routine assessment can also potentially highlight false negatives in test samples that may be indicative of mutations having functional consequences in the form of vaccine and therapeutic failures. This study highlights the importance of whole genome sequencing and expanded real-time monitoring of diagnostic PCR assays during a pandemic.