Wastewater tiling amplicon sequencing in sentinel sites reveals longitudinal dynamics of SARS-CoV-2 variants prevalence.

The ongoing evolution of SARS-CoV-2 is a significant concern, especially with the decrease in clinical sequencing efforts, which impedes the ability of public health sectors to prepare for the emergence of new variants and potential COVID-19 outbreaks. Wastewater-based epidemiology (WBE) has been proposed as a surveillance program to detect and monitor the SARS-CoV-2 variants being transmitted in communities. However, research is limited in evaluating the effectiveness of wastewater collection at sentinel sites for monitoring disease prevalence and variant dynamics, especially in terms of inferring the epidemic patterns on a broader scale, such as at the state/province level. This study utilized a multiplexed tiling amplicon-based sequencing (ATOPlex) to track the longitudinal dynamics of variant of concern (VOC) in wastewater collected from municipalities in Queensland, Australia, spanning from 2020 to 2022. We demonstrated that wastewater epidemiology measured by ATOPlex exhibited a strong and consistent correlation with the number of daily confirmed cases. The VOC dynamics observed in wastewater closely aligned with the dynamic profile reported by clinical sequencing. Wastewater sequencing has the potential to provide early warning information for emerging variants. These findings suggest that WBE at sentinel sites, coupled with sensitive sequencing methods, provides a reliable and long-term disease surveillance strategy.

Virtual Photons Shed Light on the Early Temperature of Dense QCD Matter.

Dileptons produced during heavy-ion collisions represent a unique probe of the QCD phase diagram, and convey information about the state of the strongly interacting system at the moment their preceding off-shell photon is created. In this study, we compute thermal dilepton yields from Au+Au collisions performed at different beam energies, employing a (3+1)-dimensional dynamic framework combined with emission rates accurate at next-to-leading order in perturbation theory and which include baryon chemical potential dependencies. By comparing the effective temperature extracted from the thermal dilepton invariant mass spectrum with the average temperature of the fluid, we offer a robust quantitative validation of dileptons as an effective probe of the early quark-gluon plasma stage.

Moving forward with COVID-19: Future research prospects of wastewater-based epidemiology methodologies and applications.

Wastewater-based epidemiology (WBE) has been demonstrated for its great potential in tracking of coronavirus disease 2019 (COVID-19) transmission among populations despite some inherent methodological limitations. These include non-optimized sampling approaches and analytical methods; stability of viruses in sewer systems; partitioning/retention in biofilms; and the singular and inaccurate back-calculation step to predict the number of infected individuals in the community. Future research is expected to (1) standardize best practices in wastewater sampling, analysis and data reporting protocols for the sensitive and reproducible detection of viruses in wastewater; (2) understand the in-sewer viral stability and partitioning under the impacts of dynamic wastewater flow, properties, chemicals, biofilms and sediments; and (3) achieve smart wastewater surveillance with artificial intelligence and big data models. Further specific research is essential in the monitoring of other viral pathogens with pandemic potential and subcatchment applications to maximize the benefits of WBE beyond COVID-19.

Wastewater Surveillance Can Function as an Early Warning System for COVID-19 in Low-Incidence Settings.

INTRODUCTION: During the first two years of the COVID-19 pandemic, Australia implemented a series of international and interstate border restrictions. The state of Queensland experienced limited COVID-19 transmission and relied on lockdowns to stem any emerging COVID-19 outbreaks. However, early detection of new outbreaks was difficult. In this paper, we describe the wastewater surveillance program for SARS-CoV-2 in Queensland, Australia, and report two case studies in which we aimed to assess the potential for this program to provide early warning of new community transmission of COVID-19. Both case studies involved clusters of localised transmission, one originating in a Brisbane suburb (Brisbane Inner West) in July-August 2021, and the other originating in Cairns, North Queensland in February-March 2021. MATERIALS AND METHODS: Publicly available COVID-19 case data derived from the notifiable conditions (NoCs) registry from the Queensland Health data portal were cleaned and merged spatially with the wastewater surveillance data using statistical area 2 (SA2) codes. The positive predictive value and negative predictive value of wastewater detection for predicting the presence of COVID-19 reported cases were calculated for the two case study sites. RESULTS: Early warnings for local transmission of SARS-CoV-2 through wastewater surveillance were noted in both the Brisbane Inner West cluster and the Cairns cluster. The positive predictive value of wastewater detection for the presence of notified cases of COVID-19 in Brisbane Inner West and Cairns were 71.4% and 50%, respectively. The negative predictive value for Brisbane Inner West and Cairns were 94.7% and 100%, respectively. CONCLUSIONS: Our findings highlight the utility of wastewater surveillance as an early warning tool in low COVID-19 transmission settings.

Time trends, projections, and spatial distribution of low birthweight in Australia, 2009-2030: Evidence from the National Perinatal Data Collection.

INTRODUCTION: Infants with low birthweight (LBW, birthweight <2500 g) have increased in many high-resource countries over the past two decades. This study aimed to investigate the time trends, projections, and spatial distribution of LBW in Australia, 2009-2030. METHODS: We used standard aggregate data on 3 346 808 births from 2009 to 2019 from Australia's National Perinatal Data Collection. Bayesian linear regression model was used to estimate the trends in the prevalence of LBW in Australia. RESULTS: Wefound that the prevalence of LBW was 6.18% in 2009, which has increased to 6.64% in 2019 (average annual rate of change, AARC = +0.76%). If the national trend remains the same, the projected prevalence of LBW in Australia will increase to 7.34% (95% uncertainty interval, UI = 6.99, 7.68) in 2030. Observing AARC across different subpopulations, the trend of LBW was stable among Indigenous mothers, whereas it increased among non-Indigenous mothers (AARC = +0.81%). There is also an increase among the most disadvantaged mothers (AARC = +1.08%), birthing people in either of two extreme age groups (AARC = +1.99% and +1.53% for <20 years and ≥40 years, respectively), and mothers who smoked during pregnancy (AARC = +1.52%). Spatiotemporal maps showed that some of the Statistical Area level 3 (SA3) in Northern Territory and Queensland had consistently higher prevalence for LBW than the national average from 2014 to 2019. CONCLUSION: Overall, the prevalence of LBW has increased in Australia during 2009-2019; however, the trends vary across different subpopulations. If trends persist, Australia will not achieve the Sustainable Development Goals (SDGs) target of a 30% reduction in LBW by 2030. Centering and supporting the most vulnerable subpopulations is vital to progress the SDGs and improves perinatal and infant health in Australia.

Comparison of RT-qPCR and RT-dPCR Platforms for the Trace Detection of SARS-CoV-2 RNA in Wastewater.

We compared reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and RT digital PCR (RT-dPCR) platforms for the trace detection of SARS-CoV-2 RNA in low-prevalence COVID-19 locations in Queensland, Australia, using CDC N1 and CDC N2 assays. The assay limit of detection (ALOD), PCR inhibition rates, and performance characteristics of each assay, along with the positivity rates with the RT-qPCR and RT-dPCR platforms, were evaluated by seeding known concentrations of exogenous SARS-CoV-2 in wastewater. The ALODs using RT-dPCR were approximately 2-5 times lower than those using RT-qPCR. During sample processing, the endogenous (n = 96) and exogenous (n = 24) SARS-CoV-2 wastewater samples were separated, and RNA was extracted from both wastewater eluates and pellets (solids). The RT-dPCR platform demonstrated a detection rate significantly greater than that of RT-qPCR for the CDC N1 and CDC N2 assays in the eluate (N1, p = 0.0029; N2, p = 0.0003) and pellet (N1, p = 0.0015; N2, p = 0.0067) samples. The positivity results also indicated that for the analysis of SARS-CoV-2 RNA in wastewater, including the eluate and pellet samples may further increase the detection sensitivity using RT-dPCR.

Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology.

As a cost-effective and objective population-wide surveillance tool, wastewater-based epidemiology (WBE) has been widely implemented worldwide to monitor the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater. However, viral concentrations or loads in wastewater often correlate poorly with clinical case numbers. To date, there is no reliable method to back-estimate the coronavirus disease 2019 (COVID-19) case numbers from SARS-CoV-2 concentrations in wastewater. This greatly limits WBE in achieving its full potential in monitoring the unfolding pandemic. The exponentially growing SARS-CoV-2 WBE dataset, on the other hand, offers an opportunity to develop data-driven models for the estimation of COVID-19 case numbers (both incidence and prevalence) and transmission dynamics (effective reproduction rate). This study developed artificial neural network (ANN) models by innovatively expanding a conventional WBE dataset to include catchment, weather, clinical testing coverage and vaccination rate. The ANN models were trained and evaluated with a comprehensive state-wide wastewater monitoring dataset from Utah, USA during May 2020 to December 2021. In diverse sewer catchments, ANN models were found to accurately estimate the COVID-19 prevalence and incidence rates, with excellent precision for prevalence rates. Also, an ANN model was developed to estimate the effective reproduction number from both wastewater data and other pertinent factors affecting viral transmission and pandemic dynamics. The established ANN model was successfully validated for its transferability to other states or countries using the WBE dataset from Wisconsin, USA.

SARS-CoV-2 shedding sources in wastewater and implications for wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) approach for COVID-19 surveillance is largely based on the assumption of SARS-CoV-2 RNA shedding into sewers by infected individuals. Recent studies found that SARS-CoV-2 RNA concentration in wastewater (CRNA) could not be accounted by the fecal shedding alone. This study aimed to determine potential major shedding sources based on literature data of CRNA, along with the COVID-19 prevalence in the catchment area through a systematic literature review. Theoretical CRNA under a certain prevalence was estimated using Monte Carlo simulations, with eight scenarios accommodating feces alone, and both feces and sputum as shedding sources. With feces alone, none of the WBE data was in the confidence interval of theoretical CRNA estimated with the mean feces shedding magnitude and probability, and 63% of CRNA in WBE reports were higher than the maximum theoretical concentration. With both sputum and feces, 91% of the WBE data were below the simulated maximum CRNA in wastewater. The inclusion of sputum as a major shedding source led to more comparable theoretical CRNA to the literature WBE data. Sputum discharging behavior of patients also resulted in great fluctuations of CRNA under a certain prevalence. Thus, sputum is a potential critical shedding source for COVID-19 WBE surveillance.

Detection of the Omicron (B.1.1.529) variant of SARS-CoV-2 in aircraft wastewater.

On the 26th of November 2021, the World Health Organization (WHO) designated the newly detected B.1.1.529 lineage of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the Omicron Variant of Concern (VOC). The genome of the Omicron VOC contains more than 50 mutations, many of which have been associated with increased transmissibility, differing disease severity, and potential to evade immune responses developed for previous VOCs such as Alpha and Delta. In the days since the designation of B.1.1.529 as a VOC, infections with the lineage have been reported in countries around the globe and many countries have implemented travel restrictions and increased border controls in response. We putatively detected the Omicron variant in an aircraft wastewater sample from a flight arriving to Darwin, Australia from Johannesburg, South Africa on the 25th of November 2021 via positive results on the CDC N1, CDC N2, and del(69-70) RT-qPCR assays per guidance from the WHO. The Australian Northern Territory Health Department detected one passenger onboard the flight who was infected with SARS-CoV-2, which was determined to be the Omicron VOC by sequencing of a nasopharyngeal swab sample. Subsequent sequencing of the aircraft wastewater sample using the ARTIC V3 protocol with Nanopore and ATOPlex confirmed the presence of the Omicron variant with a consensus genome that clustered with the B.1.1.529 BA.1 sub-lineage. Our detection and confirmation of a single onboard Omicron infection via aircraft wastewater further bolsters the important role that aircraft wastewater can play as an independent and unintrusive surveillance point for infectious diseases, particularly coronavirus disease 2019.

Minimizing errors in RT-PCR detection and quantification of SARS-CoV-2 RNA for wastewater surveillance.

Wastewater surveillance for pathogens using reverse transcription-polymerase chain reaction (RT-PCR) is an effective and resource-efficient tool for gathering community-level public health information, including the incidence of coronavirus disease-19 (COVID-19). Surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in wastewater can potentially provide an early warning signal of COVID-19 infections in a community. The capacity of the world's environmental microbiology and virology laboratories for SARS-CoV-2 RNA characterization in wastewater is increasing rapidly. However, there are no standardized protocols or harmonized quality assurance and quality control (QA/QC) procedures for SARS-CoV-2 wastewater surveillance. This paper is a technical review of factors that can cause false-positive and false-negative errors in the surveillance of SARS-CoV-2 RNA in wastewater, culminating in recommended strategies that can be implemented to identify and mitigate some of these errors. Recommendations include stringent QA/QC measures, representative sampling approaches, effective virus concentration and efficient RNA extraction, PCR inhibition assessment, inclusion of sample processing controls, and considerations for RT-PCR assay selection and data interpretation. Clear data interpretation guidelines (e.g., determination of positive and negative samples) are critical, particularly when the incidence of SARS-CoV-2 in wastewater is low. Corrective and confirmatory actions must be in place for inconclusive results or results diverging from current trends (e.g., initial onset or reemergence of COVID-19 in a community). It is also prudent to perform interlaboratory comparisons to ensure results' reliability and interpretability for prospective and retrospective analyses. The strategies that are recommended in this review aim to improve SARS-CoV-2 characterization and detection for wastewater surveillance applications. A silver lining of the COVID-19 pandemic is that the efficacy of wastewater surveillance continues to be demonstrated during this global crisis. In the future, wastewater should also play an important role in the surveillance of a range of other communicable diseases.

Structural Development of Human Fetal and Preterm Brain Cortical Plate Based on Population-Averaged Templates.

We hypothesized that the distinct maturational processes take place across different cortical areas from middle fetal stage to normal time of birth and these maturational processes are altered in late third trimester. Fractional anisotropies (FA) from diffusion tensor imaging (DTI) infer the microstructures of the early developing cortical plate. High-resolution DTI of 11 fetal brain specimens at postmenstrual age of 20 weeks (or simplified as 20 weeks), 19 in vivo brains at 35 weeks, and 17 in vivo brains at normal time of birth at term (40 weeks) were acquired. Population-averaged age-specific DTI templates were established with large deformation diffeomorphic metric mapping for subject groups at 20, 35, and 40 weeks. To alleviate partial volume effects, skeletonized FA values were used for mapping averaged cortical FA to the cortical surface and measuring FA at 12 functionally distinctive cortical regions. Significant and heterogeneous FA decreases take place in distinct cortical areas from 20 to 35 weeks and from 35 to 40 weeks, suggesting differentiated cortical development patterns. Temporally nonuniform FA decrease patterns during 35-40 weeks compared with those during 20-35 weeks were observed in higher-order association cortex. Measured skeletonized FA suggested dissociated changes between cerebral cortex and white matter during 35-40 weeks.

Structural development of human brain white matter from mid-fetal to perinatal stage.

The structures of developing human brain white matter (WM) tracts can be effectively quantified by DTI-derived metrics, including fractional anisotropy (FA), mean, axial and radial diffusivity (MD, AD and RD). However, dynamics of WM microstructure during very early developmental period from mid-fetal to perinatal stage is unknown. It is difficult to accurately measure microstructural properties of these WM tracts due to severe contamination from cerebrospinal fluid (CSF). In this study, high resolution DTI of fetal brains at mid-fetal stage (20 weeks of gestation or 20wg), 19 brains in the middle of 3rd trimester (35wg) and 17 brains around term (40wg) were acquired. We established first population-averaged DTI templates at these three time points and extracted WM skeleton. 16 major WM tracts in limbic, projection, commissural and association tract groups were traced with DTI tractography in native space. The WM skeleton in the template space was inversely transformed back to the native space for measuring core WM microstructures of each individual tract. Continuous microstructural enhancement and volumetric increase of WM tracts were found from 20wg to 40wg. The microstructural enhancement from FA measurement is decelerated in late 3rd trimester compared to mid-fetal to middle 3rd trimester, while volumetric increase of prefrontal WM tracts is accelerated. The microstructural enhancement from 35wg to 40wg is heterogeneous among different tract groups with microstructures of association tracts undergoing most dramatic change. Besides decreases of RD indicating active myelination, the decrease of AD for most WM tracts during late 3rd trimester suggests axonal packing process.

Enhanced CO tolerance for hydrogen activation in Au-Pt dendritic heteroaggregate nanostructures.

Au-Pt heteroaggregate nanostructures were prepared by sequential reduction methods. The structures have approximately 11 nm Au cores with Pt "tendrils" attached to the Au surface. The heteroaggregates are active H2 oxidation catalysts and show high activity at 90 degrees C in the presence of 1000 ppm CO. The surprising CO-tolerant behavior arises from the composition and unusual architecture of the particles.

The Parkland Memorial Hospital experience in ensuring compliance with Universal Newborn Hearing Screening follow-up.

OBJECTIVE: Reduce false-positive results and loss to follow-up through systematic modifications in Universal Newborn Hearing Screening at a large public hospital. STUDY DESIGN: During a pilot program, neonates who failed technician-performed automated auditory brain stem response were scheduled for diagnostic evaluation. In year 1, audiologists rescreened neonates who failed, and those who did not pass were screened as outpatients. For years 2 through 4, neonates who failed were rescreened by technicians before inpatient audiology rescreening. RESULTS: For the pilot, 3759 neonates were screened; 1% (n = 43) failed and 44% (n = 19) were lost to follow-up. In year 1, 15,297 neonates were screened and 2% (n = 365) failed; audiology rescreening reduced this to <1% (n = 129). Outpatient rescreening yielded 0.5% (n = 70) who failed; 17% (n = 12) were lost to follow-up. In year 2, 16,384 neonates were screened, 3% (n = 456) failed, and 1% (n = 167) failed after technician rescreen; audiology rescreening reduced inpatient fails to 0.6% (n = 108), and 0.4% (n = 61) failed outpatient rescreening; 11% (n = 7) were lost to follow-up. Results for years 3 and 4 were similar to year 2, with further reduction in loss to follow-up to 11% (n = 6) and 1.7% (n = 1). CONCLUSIONS: Successful Universal Newborn Hearing Screening with reduced false-positive results and loss to follow-up can be accomplished with a planned schedule of inpatient rescreens and outpatient rescreening at the birthing facility.