Improved performance of nucleic acid-based assays for genetically diverse norovirus surveillance.

Nucleic acid-based assays, such as polymerase chain reaction (PCR), that amplify and detect organism-specific genome sequences are a standard method for infectious disease surveillance. However, challenges arise for virus surveillance because of their genetic diversity. Here, we calculated the variability of nucleotides within the genomes of 10 human viral species in silico and found that endemic viruses exhibit a high percentage of variable nucleotides (e.g., 51.4% for norovirus genogroup II). This genetic diversity led to the variable probability of detection of PCR assays (the proportion of viral sequences that contain the assay's target sequences divided by the total number of viral sequences). We then experimentally confirmed that the probability of the target sequence detection is indicative of the number of mismatches between PCR assays and norovirus genomes. Next, we developed a degenerate PCR assay that detects 97% of known norovirus genogroup II genome sequences and recognized norovirus in eight clinical samples. By contrast, previously developed assays with 31% and 16% probability of detection had 1.1 and 2.5 mismatches on average, respectively, which negatively impacted RNA quantification. In addition, the two PCR assays with a lower probability of detection also resulted in false negatives for wastewater-based epidemiology. Our findings suggest that the probability of detection serves as a simple metric for evaluating nucleic acid-based assays for genetically diverse virus surveillance.IMPORTANCENucleic acid-based assays, such as polymerase chain reaction (PCR), that amplify and detect organism-specific genome sequences are employed widely as a standard method for infectious disease surveillance. However, challenges arise for virus surveillance because of the rapid evolution and genetic variation of viruses. The study analyzed clinical and wastewater samples using multiple PCR assays and found significant performance variation among the PCR assays for genetically diverse norovirus surveillance. This finding suggests that some PCR assays may miss detecting certain virus strains, leading to a compromise in detection sensitivity. To address this issue, we propose a metric called the probability of detection, which can be simply calculated in silico using a code developed in this study, to evaluate nucleic acid-based assays for genetically diverse virus surveillance. This new approach can help improve the sensitivity and accuracy of virus detection, which is crucial for effective infectious disease surveillance and control.

The microelectronic wireless nitrate sensor network for environmental water monitoring.

Quantitative monitoring of water conditions in a field is a critical ability for environmental science studies. We report the design, fabrication and testing of a low cost, miniaturized and sensitive electrochemical based nitrate sensor for quantitative determination of nitrate concentrations in water samples. We have presented detailed analysis for the nitrate detection results using the miniaturized sensor. We have also demonstrated the integration of the sensor to a wireless network and carried out field water testing using the sensor. We envision that the field implementation of the wireless water sensor network will enable "smart farming" and "smart environmental monitoring".

Application of neighborhood-scale wastewater-based epidemiology in low COVID-19 incidence situations.

Wastewater-based epidemiology (WBE), an emerging approach for community-wide COVID-19 surveillance, was primarily characterized at large sewersheds such as wastewater treatment plants serving a large population. Although informed public health measures can be better implemented for a small population, WBE for neighborhood-scale sewersheds is less studied and not fully understood. This study applied WBE to seven neighborhood-scale sewersheds (average population of 1471) from January to November 2021. Community testing data showed an average of 0.004 % incidence rate in these sewersheds (97 % of monitoring periods reported two or fewer daily infections). In 92 % of sewage samples, SARS-CoV-2 N gene fragments were below the limit of quantification. We statistically determined 10-2.6 as the threshold of the SARS-CoV-2 N gene concentration normalized to pepper mild mottle virus (N/PMMOV) to alert high COVID-19 incidence rate in the studied sewershed. This threshold of N/PMMOV identified neighborhood-scale outbreaks (COVID-19 incidence rate higher than 0.2 %) with 82 % sensitivity and 51 % specificity. Importantly, neighborhood-scale WBE can discern local outbreaks that would not otherwise be identified by city-scale WBE. Our findings suggest that neighborhood-scale WBE is an effective community-wide disease surveillance tool when COVID-19 incidence is maintained at a low level.

Microbial diversity in an intensively managed landscape is structured by landscape connectivity.

Intensively managed land increases the rate of nutrient and particle transport within a basin, but the impact of these changes on microbial community assembly patterns at the basin scale is not yet understood. The objective of this study was to investigate how landscape connectivity and dispersal impacts microbial diversity in an agricultural-dominated watershed. We characterized soil, sediment and water microbial communities along the Upper Sangamon River basin in Illinois-a 3600 km2 watershed strongly influenced by human activity, especially landscape modification and extensive fertilization for agriculture. We employed statistical and network analyses to reveal the microbial community structure and interactions in the critical zone (water, soil and sediment media). Using a Bayesian source tracking approach, we predicted microbial community connectivity within and between the environments. We identified strong connectivity within environments (up to 85.4 ± 13.3% of sequences in downstream water samples sourced from upstream samples, and 44.7 ± 26.6% in soil and sediment samples), but negligible connectivity across environments, which indicates that microbial dispersal was successful within but not between environments. Species sorting based on sample media type and environmental parameters was the dominant driver of community dissimilarity. Finally, we constructed operational taxonomic unit association networks for each environment and identified a number of co-occurrence relationships that were shared between habitats, suggesting that these are likely to be ecologically significant.