A framework for automated anomaly detection in high frequency water-quality data from in situ sensors

River water-quality monitoring is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values. However, anomalies caused by technical issues confound these data, while the volume and velocity of data prevent manual detection. We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data. After identifying end-user needs and defining anomalies, we ranked their importance and selected suitable detection methods. High priority anomalies included sudden isolated spikes and level shifts, most of which were classified correctly by regression-based methods such as autoregressive integrated moving average models. However, using other water-quality variables as covariates reduced performance due to complex relationships among variables. Classification of drift and periods of anomalously low or high variability improved when we applied replaced anomalous measurements with forecasts, but this inflated false positive rates. Feature-based methods also performed well on high priority anomalies, but were also less proficient at detecting lower priority anomalies, resulting in high false negative rates. Unlike regression-based methods, all feature-based methods produced low false positive rates, but did not and require training or optimization. Rule-based methods successfully detected impossible values and missing observations. Thus, we recommend using a combination of methods to improve anomaly detection performance, whilst minimizing false detection rates. Furthermore, our framework emphasizes the importance of communication between end-users and analysts for optimal outcomes with respect to both detection performance and end-user needs. Our framework is applicable to other types of high frequency time-series data and anomaly detection applications

Interactions among multiple stressors vary with exposure duration and biological response

Coastal ecosystems are exposed to multiple anthropogenic stressors. Effective management actions would be better informed from generalized predictions of the individual, combined and interactive effects of multiple stressors; however, few generalities are shared across different meta-analyses. Using an experimental study, we present an approach for analysing regression-based designs with generalized additive models that allowed us to capture nonlinear effects of exposure duration and stressor intensity and access interactions among stressors. We tested the approach on a globally distributed marine diatom, using 72 h photosynthesis and growth assays to quantify the individual and combined effects of three common water quality stressors; photosystem II-inhibiting herbicide exposure, dissolved inorganic nitrogen (DIN) enrichment and reduced light (due to excess suspended sediment). Exposure to DIN and reduced light generally resulted in additivity, while exposure to diuron and reduced light resulted in additive, antagonistic or synergistic interactions, depending on the stressor intensity, exposure period and biological response. We thus find the context of experimental studies to be a primary driver of interactions. The experimental and modelling approaches used here bridge the gap between two-way designs and regression-based studies, which provides a way forward to identify generalities in multiple stressor interactions

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials

Studies of black diamond as an antibacterial surface for gram negative bacteria: the interplay between chemical and mechanical bactericidal activity

‘Black silicon’ (bSi) samples with surfaces covered in nanoneedles of length ~5 μm were fabricated using a plasma etching process and then coated with a conformal uniform layer of diamond using hot filament chemical vapour deposition to produce ‘black diamond’ (bD) nanostructures. The diamond needles were then chemically terminated with H, O, NH2 or F using plasma treatment, and the hydrophilicity of the resulting surfaces were assessed using water droplet contact-angle measurements, and scaled in the order O > H ≈NH2 >F, with the F-terminated surface being superhydrophobic. The effectiveness of these differently terminated bD needles in killing the Gram-negative bacterium E. coli was semiquantified by Live/Dead staining and fluorescence microscopy, and visualised by environmental scanning electron microscopy. The total number of adhered bacteria was consistent for all the nanostructured bD surfaces at around 50% of the value for the flat diamond control. This, combined with a chemical bactericidal effect of 20–30%, shows that the nanostructured bD surfaces supported significantly fewer viable E. coli than flat surfaces. Moreover, the bD surfaces were particularly effective at preventing the establishment of bacterial aggregates – a precursor to biofilm formation. The percentage of dead bacteria also decreased as a function of hydrophilicity. These results are consistent with a predominantly mechanical mechanism for bacteria death based on the stretching and disruption of the cell membrane, combined with an additional effect from the chemical nature of the surface

Large-scale Phenotyping of Patients With Long Covid Post-hospitalization Reveals Mechanistic Subtypes of Disease

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Automated High-Content Live Animal Drug Screening Using C. elegans Expressing the Aggregation Prone Serpin α1-antitrypsin Z

The development of preclinical models amenable to live animal bioactive compound screening is an attractive approach to discovering effective pharmacological therapies for disorders caused by misfolded and aggregation-prone proteins. In general, however, live animal drug screening is labor and resource intensive, and has been hampered by the lack of robust assay designs and high throughput work-flows. Based on their small size, tissue transparency and ease of cultivation, the use of C. elegans should obviate many of the technical impediments associated with live animal drug screening. Moreover, their genetic tractability and accomplished record for providing insights into the molecular and cellular basis of human disease, should make C. elegans an ideal model system for in vivo drug discovery campaigns. The goal of this study was to determine whether C. elegans could be adapted to high-throughput and high-content drug screening strategies analogous to those developed for cell-based systems. Using transgenic animals expressing fluorescently-tagged proteins, we first developed a high-quality, high-throughput work-flow utilizing an automated fluorescence microscopy platform with integrated image acquisition and data analysis modules to qualitatively assess different biological processes including, growth, tissue development, cell viability and autophagy. We next adapted this technology to conduct a small molecule screen and identified compounds that altered the intracellular accumulation of the human aggregation prone mutant that causes liver disease in α1-antitrypsin deficiency. This study provides powerful validation for advancement in preclinical drug discovery campaigns by screening live C. elegans modeling α1-antitrypsin deficiency and other complex disease phenotypes on high-content imaging platforms