Surveillance and correlation of SARS-CoV-2 viral RNA, antigen, virus isolation, and self-reported symptoms in a longitudinal study with daily sampling

The novel coronavirus pandemic incited unprecedented demand for assays that detect viral nucleic acids, viral proteins, and corresponding antibodies. The 320 molecular diagnostics in receipt of FDA emergency use authorization mainly focus on viral detection; however, no currently approved test can be used to infer infectiousness, i.e., the presence of replicable virus. As the number of tests conducted increased, persistent SARS-CoV-2 RNA positivity by RT-PCR in some individuals led to concerns over quarantine guidelines. To this end, we attempted to design an assay that reduces the frequency of positive test results from individuals who do not shed culturable virus. We describe multiplex quantitative RT-PCR (qRT-PCR) assays that detect genomic RNA (gRNA) and subgenomic RNA (sgRNA) species of SARS-CoV-2, including spike (S), nucleocapsid (N), membrane (M), envelope (E), and ORF8. The absolute copy number of each RNA target was determined in longitudinal specimens from a household transmission study. Calculated viral RNA levels over the 14-day follow up period were compared with antigen testing and self-reported symptoms to characterize the clinical and molecular dynamics of infection and infer predictive values of these qRT-PCR assays relative to culture isolation. When detection of sgS RNA was added to the CDC 2019-Novel Coronavirus Real-Time RT-PCR Diagnostic Panel, we found a qRT-PCR positive result was 98% predictive of a positive culture (negative predictive value was 94%). Our findings suggest sgRNA presence correlates with active infection, may help identify individuals shedding culturable virus, and that similar multiplex assays can be adapted to current and future variants.

Lateral detrital C transfer across a Spartina alterniflora invaded estuarine wetland

Background The lateral movements of mass and energy across the terrestrial-aquatic interface are being increasingly recognized for their importance in the carbon (C) balance of coastal/estuarine wetlands. We quantified the lateral flux of detrital C in the Yangtze estuary where invasive Spartina alterniflora has substantially and extensively altered the ecosystem structure and functions. Our overall objective was to close the C budget of estuarine wetlands through field sampling, tower-based measurements, and modeling. Methods A lateral detrital C exchange evaluation platform was established in a case study of the Yangtze River Estuary to investigate the effect of ecosystem structural changes on lateral detrital C transfer processes. This study estimated the lateral detrital C exchange based on the gross primary production (GPP) by performing coupled modeling and field sampling. Tower-based measurements and MODIS time series and CH4 outgassing and biomass simultaneously measured the lateral detrital C flux to characterize the relative contributions of lateral (i.e., detritus) C fluxes to the annual marsh C budget. Results The C pools in the plants and soil of Spartina marshes were significantly higher than those of the native community dominated by Phragmites australis. The GPP based on MODIS (GPPMODIS) was 472.6 g C m−2 year−1 and accounted for 73.0% of the GPP estimated from eddy covariance towers (GPPEC) (646.9 ± 70.7 g C m−2 year−1). We also detected a higher GPPMODIS during the pre-growing season, which exhibited a similar lateral detrital C flux magnitude. On average, 25.8% of the net primary production (NPP), which ranged from 0.21 to 0.30 kg C m−2 year−1, was exported during lateral exchange. The annual C loss as CH4 was estimated to be 17.9 ± 3.7 g C m−2 year−1, accounting for 2.8% of the GPPEC. The net positive detrital C flux (i.e., more detritus leaving the wetlands), which could exceed 0.16 kg C m−2 day−1, was related to daily tides. However, the observed lateral detrital C flux based on monthly sampling was 73.5% higher than that based on daily sampling (i.e., the sum of daily sampling), particularly in March and October. In addition, spatiotemporal granularities were responsible for most of the uncertainty in the lateral detrital C exchange. Conclusion This research demonstrated that an integrated framework incorporating modeling and field sampling can quantitatively assess lateral detrital C transport processes across the terrestrial-aquatic interface in estuarine wetlands. However, we note some limitations in the application of the light-use efficiency model to tidal wetlands. Spartina invasion can turn the lateral C balance from a C source (209.0 g C m−2 year−1) of Phragmites-dominated marshes into a small C sink (-31.0 g C m−2 year−1). Sampling over a more extended period and continuous measurements are essential for determining the contribution of different lateral detrital C flux processes to closing the ecosystem C budgets. The sampling spatiotemporal granularities can be key to assessing lateral detrital C transfer.

SARS-CoV-2 within-host and in-vitro genomic variability and sub-genomic RNA levels indicate differences in viral expression between clinical and in-vitro cohorts.

Background: Low frequency intrahost single nucleotide variants (iSNVs) of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) have been increasingly recognised as predictive indicators of positive selection. Particularly as growing numbers of SARS-CoV-2 variants of interest (VOI) and concern (VOC) emerge. However, the dynamics of subgenomic RNA (sgRNA) expression and its impact on genomic diversity and infection outcome remain poorly understood. This study aims to investigate and quantify iSNVs and sgRNA expression in single and longitudinally sampled cohorts over the course of mild and severe SARS-CoV-2 infection benchmarked against an in-vitro infection model. Methods: Two clinical cohorts of SARS-CoV-2 positive cases in New South Wales, Australia collected between March 2020 and August 2021 were sequenced. Longitudinal samples from cases hospitalised due to SARS-CoV-2 infection (severe) were analysed and compared with cases that presented with SARS-CoV-2 symptoms but were not hospitalised (mild). SARS-CoV-2 genomic diversity profiles were also examined from daily sampling of culture experiments for three SARS-CoV-2 variants (Lineage A, B.1.351, and B.1.617.2) cultured in VeroE6 C1008 cells (n = 33). Results: ISNVs were detected in 83% (19/23) of the mild cohort cases and 100% (16/16) of the severe cohort cases. SNP profiles remained relatively fixed over time, with an average of 1.66 SNPs gained or lost and an average of 4.2 and 5.9 low frequency variants per patient were detected in severe and mild infection, respectively. SgRNA was detected in 100% (25/25) of the mild genomes and 92% (24/26) of the severe genomes. Total sgRNA expressed across all genes in the mild cohort was significantly higher than that of the severe cohort. Significantly higher expression levels were detected in the spike and the nucleocapsid genes. There was significantly less sgRNA detected in the culture cohort than the clinical. Discussion and Conclusions: The positions and frequencies of iSNVs in the severe and mild infection cohorts were dynamic overtime, highlighting the importance of continual monitoring, particularly during community outbreaks where multiple SARS-Cov-2 variants may co-circulate. SgRNA levels can vary across patients and the overall level of sgRNA reads compared to genomic RNA can be less than 1%. The relative contribution of sgRNA to the severity of illness warrants further investigation given the level of variation between genomes. Further monitoring of sgRNAs will improve the understanding of SARS-CoV-2 evolution and the effectiveness of therapeutic and public health containment measures during the pandemic.

Ocean Response to Super-Typhoon Haiyan

Present paper studies the ocean response to super-typhoon Haiyan based on satellite and Argo float data. First, we show the satellite-based surface wind and sea surface temperature during super-typhoon Haiyan, and evaluate the widely-used atmospheric and oceanic analysis-or-reanalysis datasets. Second, we investigate the signals of Argo float, and find the daily-sampling Argo floats capture the phenomena of both vertical-mixing-induced mixed-layer extension and nonlocal subsurface upwelling. Accordingly, the comparisons between Argo float and ocean reanalysis reveal that, the typhoon-induced upwelling in the ocean reanalysis needs to be further improved, meanwhile, the salinity profiles prior to typhoon arrival are significantly biased.

Longitudinal assessment of SARS-CoV-2 nucleocapsid antigenemia in patients hospitalized with COVID-19

While RT-PCR tests of nasopharyngeal swabs remain the gold standard for the detection of SARS-CoV-2 infection and monitoring of COVID-19 disease progression, measurement of nucleocapsid antigenemia in serum and plasma samples is an underexplored alternative proxy for disease severity. To explore the dynamics of nucleocapsid antigenemia, we measured levels of nucleocapsid antigen using a highly sensitive Single Molecule Array (Simoa) assay in 817 serially collected serum and plasma samples from 93 PCR-confirmed COVID-19 patients for whom symptom onset date could be extracted by chart review. In a subset of these individuals (n=13), we measured seroconversion by titering for receptor binding domain (RBD) specific IgG, IgA, and IgM. A model of exponential decay was fit to data from individuals with high resolution daily sampling (N=34), from which the half-life of SARS-CoV-2 nucleocapsid in serum was determined. Mean nucleocapsid half-life in this group of patients was 1.17 days (SD=0.82). Nucleocapsid levels were significantly higher in the first 10 days following symptom onset in patients who died compared to those with a milder disease course (p=0.004). Further, mortality was associated with a trend toward longer nucleocapsid half-life (1.51 days vs. 0.79 days) (p=0.10). In patients who had both antibody and antigenemia data available, antibody response was temporally linked to antigen decay, reaching peak levels as antigen was cleared from the blood. Our data identify SARS-CoV-2 nucleocapsid antigenemia as a potential diagnostic tool for acute COVID-19 disease and an early biomarker associated with disease severity.

Daily sampling of early SARS-CoV-2 infection reveals substantial heterogeneity in infectiousness

The dynamics of SARS-CoV-2 replication and shedding in humans remain poorly understood. We captured the dynamics of infectious virus and viral RNA shedding during acute infection through daily longitudinal sampling of 60 individuals for up to 14 days. By fitting mechanistic models, we directly estimate viral reproduction and clearance rates, and overall infectiousness for each individual. Significant person-to-person variation in infectious virus shedding suggests that individual-level heterogeneity in viral dynamics contributes to superspreading. Viral genome load often peaked days earlier in saliva than in nasal swabs, indicating strong compartmentalization and suggesting that saliva may serve as a superior sampling site for early detection of infection. Viral loads and clearance kinetics of B.1.1.7 and non-B.1.1.7 viruses were indistinguishable, however B.1.1.7 exhibited a significantly slower pre-peak growth rate in saliva. These results provide a high-resolution portrait of SARS-CoV-2 infection dynamics and implicate individual-level heterogeneity in infectiousness in superspreading.

Seasonal variation in the diel activity of a dung beetle assemblage

The seasonal and diel variations of dung beetle species were studied in an Iberian mid-mountain locality to examine the interaction between these two temporal rhythms. We assume that a seasonal variation in the diel activity would support the notion that both rhythms may assist in achieving a quick and flexible response when the climatic conditions change. Data coming from 4,104 pitfall traps placed during 15 sampling periods and totalling 30 daily sampling cycles were analysed using circular statistics and General Linear Models. A wide variety of seasonal patterns are observed, highlighting those species with a clear unimodal or spring-autumn bimodal seasonal pattern. However, a midday diel pattern is the norm in most of the species, except in the case of those exhibiting a high body weight that prefer dusk or night periods. We hypothesize that most of the dung beetle species fly at noon to promote the passive heating of their muscle activity and minimize the metabolic energy expenditure. Results only partially support the seasonal variation in diel activity. Diel preferences are mainly manifested at the time of the year in which the abundance is greater. Approximately two-thirds of the considered species exhibit a similar diel activity along their seasonal active period. As consequence, a significant portion of the dung beetle species currently inhabiting Mediterranean mid-mountains are not able to use the daily variation in climatic conditions to limit the inconveniences of climate change.

Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins

Basin-centric long short-term memory (LSTM) network models have recently been shown to be an exceptionally powerful tool for simulating stream temperature (Ts, temperature measured in rivers), among other hydrological variables. However, spatial extrapolation is a well-known challenge to modeling Ts and it is uncertain how an LSTM-based daily Ts model will perform in unmonitored or dammed basins. Here we compiled a new benchmark dataset consisting of >400 basins for across the contiguous United States in different data availability groups (DAG, meaning the daily sampling frequency) with or without major dams and study how to assemble suitable training datasets for predictions in monitored or unmonitored situations. For temporal generalization, CONUS-median best root-mean-square error (RMSE) values for sites with extensive (99%), intermediate (60%), scarce (10%) and absent (0%, unmonitored) data for training were 0.75, 0.83, 0.88, and 1.59°C, representing the state of the art. For prediction in unmonitored basins (PUB), LSTM’s results surpassed those reported in the literature. Even for unmonitored basins with major reservoirs, we obtained a median RMSE of 1.492°C and an R2 of 0.966. The most suitable training set was the matching DAG that the basin could be grouped into, e.g., the 60% DAG for a basin with 61% data availability. However, for PUB, a training dataset including all basins with data is preferred. An input-selection ensemble moderately mitigated attribute overfitting. Our results suggest there are influential latent processes not sufficiently described by the inputs (e.g., geology, wetland covers), but temporal fluctuations are well predictable, and LSTM appears to be the more accurate Ts modeling tool when sufficient training data are available.

Sampling Frequency Is a Key Factor to the Efficient Actuation of the Phytohormones in the Increase of Biomass and Macromolecules Production by Spirulina (Arthorspira)

Application of microalgae in industries is limited by their low biomass production and low macromolecule content. Phytohormones are being studied to solve these problems, however is necessary to define conditions that enhance the results for this kind of cultivation strategy. The increasing of the contact time between substances and cells can be a factor of the improvement in the phytohormone uptake by microalgae. We hypothesize that by decreasing the sampling frequency for monitoring culture parameters, we would also be interfering less in the uptake of phytohormones by microalgae. Therefore, the present study aimed to investigate the influence of sampling frequency on the production of biomass and biomolecules of Spirulina sp. LEB 18 in cultivations supplemented with trans-zeatin and indole-3-acetic acid (IAA). Treatments with lower sampling frequency and supplementation of 1 mg L− 1 IAA enhanced biomass accumulation by 75 %, carbohydrate content by 50 %, and protein content by 30 % as compared to control experiment daily sampling. In addition, sampling every 10 days with 0,01 mg L− 1 IAA supplementation increased the lipid content by 51 % as compared with the experiment without phytohormone and daily sample removal. Therefore, we developed a new strategy to supplement phytohormones and improve microalgal production. This investigation presented a cultivation system efficient, which can be easy to implement in industries because of no need to change the operational aspects of the cultivation. Furthermore, the strategy will can be very useful to enhance the production capacity of microalgae on a large-scale.

757 Does Increased Sampling of Cerebrospinal Fluid from External Ventricular Drains Lead to Increased Rates of Infection?

Introduction External ventricular drains (EVDs) are commonly used in the management of acute hydrocephalus after subarachnoid haemorrhage (SAH). Infection is the most common complication. There remains controversy over whether frequent sampling is associated with increased risk of infection. Method Two cohorts of patients requiring EVD after SAH were retrospectively analysed for suspected and proven CSF infection. The first clinical cohort was of 50 consecutive patients with twice weekly sampling. The second group had alternate daily sampling as part of a prospective research study. Results Female to male ratio were (32:18) and (29:15) in clinical vs research group respectively. Average age of both groups was 59. Average length of treatment with EVD in both groups was 10 days. 16/50 (32%) patients had a suspected infection vs 13/44 (30%) and 8/50 (16%) had a proven infection compared to 6/44 (14%) in clinical and research groups, respectively. There was no statistically significant difference between the two groups (suspected infections p = 0.7 and proven infections p = 0.7) Conclusions Increased rates in CSF sampling in the research cohort did not result in higher rates of CSF infection. This suggests that rate of sampling, if done following a strict protocol, is not associated with increased risk of infection.