Evaluation of the IgG antibody response to SARS CoV-2 infection and performance of a lateral flow immunoassay: cross-sectional and longitudinal analysis over 11 months.

OBJECTIVE: To evaluate the dynamics and longevity of the humoral immune response to SARS-CoV-2 infection and assess the performance of professional use of the UK-RTC AbC-19 Rapid Test lateral flow immunoassay (LFIA) for the target condition of SARS-CoV-2 spike protein IgG antibodies. DESIGN: Nationwide serological study. SETTING: Northern Ireland, UK, May 2020-February 2021. PARTICIPANTS: Plasma samples were collected from a diverse cohort of individuals from the general public (n=279), Northern Ireland healthcare workers (n=195), pre-pandemic blood donations and research studies (n=223) and through a convalescent plasma programme (n=183). Plasma donors (n=101) were followed with sequential samples over 11 months post-symptom onset. MAIN OUTCOME MEASURES: SARS-CoV-2 antibody levels in plasma samples using Roche Elecsys Anti-SARS-CoV-2 IgG/IgA/IgM, Abbott SARS-CoV-2 IgG and EuroImmun IgG SARS-CoV-2 ELISA immunoassays over time. UK-RTC AbC-19 LFIA sensitivity and specificity, estimated using a three-reference standard system to establish a characterised panel of 330 positive and 488 negative SARS-CoV-2 IgG samples. RESULTS: We detected persistence of SARS-CoV-2 IgG antibodies for up to 10 months post-infection, across a minimum of two laboratory immunoassays. On the known positive cohort, the UK-RTC AbC-19 LFIA showed a sensitivity of 97.58% (95.28% to 98.95%) and on known negatives, showed specificity of 99.59% (98.53 % to 99.95%). CONCLUSIONS: Through comprehensive analysis of a cohort of pre-pandemic and pandemic individuals, we show detectable levels of IgG antibodies, lasting over 46 weeks when assessed by EuroImmun ELISA, providing insight to antibody levels at later time points post-infection. We show good laboratory validation performance metrics for the AbC-19 rapid test for SARS-CoV-2 spike protein IgG antibody detection in a laboratory-based setting.

An efficient Chlorella sp.-Cupriavidus necator microcosm for phenol degradation and its cooperation mechanism.

A Chlorella sp.-Cupriavidus necator (C. necator) microcosm was artificially established for phenol degradation. The cooperation relationship between Chlorella sp. and C. necator was initially demonstrated, and then the effects of Chlorella sp./C. necator inoculation ratio, light intensity, temperature and pH on the performance of this microcosm were systematically evaluated and optimized. The optimal conditions for phenol degradation were as follows: a Chlorella sp./C. necator inoculation ratio of 1:1, a light intensity of 110 µmol m-2 s-1, a temperature in the range of 25-32 °C and a pH in the range of 5.5-7.5. Under optimal conditions, this microcosm could degrade phenol with a maximum concentration of 1200 mg L-1 within 60 h. It was found that only when the phenol concentration was reduced to the tolerance concentration of microalgae, that is, the last stage of phenol degradation, the cooperation effect could be generated, indicating that the tolerance of microalgae to phenol may be more important than its degradation performance. Comparative transcriptomic analysis was conducted to discuss the cooperation mechanism of this microcosm subject to high phenol concentrations. The up-regulation of genes involved in photosynthesis and carbon fixation of Chlorella sp. demonstrated the CO2 and O2 exchange between Chlorella sp. and C. necator and their cooperation relationship. This study suggests that this microcosm has great potential for the bioremediation of phenol contaminants.

Integrating modelling and smart sensors for environmental and human health.

Sensors are becoming ubiquitous in everyday life, generating data at an unprecedented rate and scale. However, models that assess impacts of human activities on environmental and human health, have typically been developed in contexts where data scarcity is the norm. Models are essential tools to understand processes, identify relationships, associations and causality, formalize stakeholder mental models, and to quantify the effects of prevention and interventions. They can help to explain data, as well as inform the deployment and location of sensors by identifying hotspots and areas of interest where data collection may achieve the best results. We identify a paradigm shift in how the integration of models and sensors can contribute to harnessing 'Big Data' and, more importantly, make the vital step from 'Big Data' to 'Big Information'. In this paper, we illustrate current developments and identify key research needs using human and environmental health challenges as an example.

Investigation of river eutrophication as part of a low dissolved oxygen total maximum daily load implementation.

In the United States, environmentally impaired rivers are subject to regulation under total maximum daily load (TMDL) regulations that specify watershed wide water quality standards. In California, the setting of TMDL standards is accompanied by the development of scientific and management plans directed at achieving specific water quality objectives. The San Joaquin River (SJR) in the Central Valley of California now has a TMDL for dissolved oxygen (DO). Low DO conditions in the SJR are caused in part by excessive phytoplankton growth (eutrophication) in the shallow, upstream portion of the river that create oxygen demand in the deeper estuary. This paper reports on scientific studies that were conducted to develop a mass balance on nutrients and phytoplankton in the SJR. A mass balance model was developed using WARMF, a model specifically designed for use in TMDL management applications. It was demonstrated that phytoplankton biomass accumulates rapidly in a 88 km reach where plankton from small, slow moving tributaries are diluted and combined with fresh nutrient inputs in faster moving water. The SJR-WARMF model was demonstrated to accurately predict phytoplankton growth in the SJR. Model results suggest that modest reductions in nutrients alone will not limit algal biomass accumulation, but that combined strategies of nutrient reduction and algal control in tributaries may have benefit. The SJR-WARMF model provides stakeholders a practical, scientific tool for setting remediation priorities on a watershed scale.

Miniaturization of the flowing fluid electric conductivity logging technique.

An understanding of the hydraulic properties of the aquifer and the depth distribution of salts is critical for evaluating the potential of ground water for conjunctive water use and for maintaining suitable ground water quality in agricultural regions where ground water is used extensively for irrigation and drinking water. The electrical conductivity profiles recorded in a well using the flowing fluid electric conductivity (FEC) logging method can be analyzed to estimate interval-specific hydraulic conductivity and estimates of the salinity concentration with depth. However, operating irrigation wells commonly allow limited access, and the traditional equipment used for FEC logging cannot fit through the small access pipe intersecting the well. A modified, miniaturized FEC logging technique was developed for use in wells with limited access. In addition, a new method for injecting water over the entire screened interval of the well reduces the time required to perform FEC logging.