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Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll‐a (chl‐a) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl‐a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl‐a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel‐2 (S2) and Sentinel‐3 (S3) to map chl‐a and cyanobacteria, respectively, classified chl‐a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in California's water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%–79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%–80% false positive rate and 0%–38% false negative rate, depending on algorithm. Using SRS‐based chl‐a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone. Plain Language Summary: Lakes often have algal blooms that create a water quality concern, especially when they contain cyanobacteria, which can be toxic to both humans and animals. These harmful algal blooms are of great concern in areas with limited water supply in states such as California. While it is often difficult and costly to collect and monitor toxin concentrations, monitoring concentrations of chlorophyll‐a (chl‐a) –a measure of how much algae are present—is relatively common and can even be accomplished using satellite remote sensing. There have been multiple studies that have found a relationship between toxins produced by cyanobacteria and chl‐a. The World Health Organization (WHO) has recently released (2021) an updated release of their previous 1999 guidance values for toxin monitoring based on chl‐a concentration. With satellite data, we were able to measure chl‐a concentration in a major reservoir in California, and then classify the chl‐a measurements into the WHO's guidance values for toxins. We compared the satellite‐based guidance values to the public advisory levels currently set by the California Department of Water Resources. Our results indicate that SRS of chl‐a is a reasonable substitute for cyanobacteria toxin advisories, and our framework can be applied to similar cyanobacteria dominated lakes. Key Points: The World Health Organization (WHO) updated cyanobacteria harmful algal blooms (cyanoHABs) guidelines for chlorophyll‐a (chl‐a) as a proxyWith satellite remote sensing (SRS), we estimated and classified chl‐a to compare cyanotoxins advisories used by CaliforniaThis study provides a framework for evaluating public health utility of SRS for enhancing cyanotoxin monitoring globally [ABSTRACT FROM AUTHOR]

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides remotely-sensed estimates of evapotranspiration at 70 m spatial resolution every 1–5 days, sampling across the diurnal cycle. This study, in partnership with an operational water management organization, the Eastern Municipal Water District (EMWD) in Southern California, was conducted to evaluate estimates of evapotranspiration under ideal conditions where water is not limited. EMWD regularly uses a ground-based network of reference evapotranspiration (ETo) from the California Irrigation Management Information System (CIMIS); yet, there are gaps in spatial coverage and questions of spatial representativeness and consistency. Space-based potential evapotranspiration (PET) estimates, such as those from ECOSTRESS, provide consistent spatial coverage. We compared ECOSTRESS ETo (estimated from PET) to CIMIS ETo at five CIMIS sites in Riverside County, California from July 2018–June 2020. We found strong correlations between CIMIS ETo and ECOSTRESS ETo across all five sites (R2 = 0.89, root mean square error (RMSE) = 0.11 mm hr−1). Both CIMIS and ECOSTRESS ETo captured similar seasonal patterns through the study period as well as diurnal variability. There were site-specific differences in the relationship between ECOSTRESS AND CIMIS, in part due to spatial heterogeneity around the station site. Consequently, careful examination of landscapes surrounding CIMIS sites must be considered in future comparisons. These results indicate that ECOSTRESS successfully retrieves PET that is comparable to ground-based reference ET, highlighting the potential for providing observation-driven guidance for irrigation management across spatial scales. [ABSTRACT FROM AUTHOR]

Introduction: About 42 million surgeries are performed annually in the USA. While the postoperative mortality is less than 2%, 12% of all patients in the high-risk surgery group account for 80% of postoperative deaths. New onset of haemodynamic instability is common in surgical patients and its delayed treatment leads to increased morbidity and mortality. The goal of this proposal is to develop, validate and test real-time intraoperative risk prediction tools based on clinical data and high-fidelity physiological waveforms to predict haemodynamic instability during surgery., Methods and Analysis: We will initiate our work using an existing annotated intraoperative database from the University of California Irvine, including clinical and high-fidelity waveform data. These data will be used for the training and development of the machine learning model (Carnegie Mellon University) that will then be tested on prospectively collected database (University of California Los Angeles). Simultaneously, we will use existing knowledge of haemodynamic instability patterns derived from our intensive care unit cohorts, medical information mart for intensive care II data, University of California Irvine data and animal studies to create smart alarms and graphical user interface for a clinical decision support. Using machine learning, we will extract a core dataset, which characterises the signatures of normal intraoperative variability, various haemodynamic instability aetiologies and variable responses to resuscitation. We will then employ clinician-driven iterative design to create a clinical decision support user interface, and evaluate its effect in simulated high-risk surgeries., Ethics and Dissemination: We will publish the results in a peer-reviewed publication and will present this work at professional conferences for the anaesthesiology and computer science communities. Patient-level data will be made available within 6 months after publication of the primary manuscript. The study has been approved by University of California, Los Angeles Institutional review board. (IRB #19-0 00 354)., Competing Interests: Competing interests: MC is a consultant for Edwards Lifesciences and Masimo, and has funded research from Edwards Lifesciences and Masimo. He is also the founder of Sironis owns patents and receive royalties for closed loop haemodynamic management that have been licensed to Edwards Lifesciences. MC department receives funding from the NIH (R01GM117622; R01 NR013012; U54HL119893; 1R01HL144692). JR is the founder of Sironis owns patents and receive royalties for closed loop haemodynamic management that have been licensed to Edwards Lifesciences. CL is an engineer at Edwards Lifesciences., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart., Methods: We report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery. Electronic health record data from 53 097 surgical patients (2.01% mortality rate) who underwent general anaesthesia between April 1, 2013 and December 10, 2018 in a large US academic medical centre were used to extract 58 preoperative features., Results: Using a random forest classifier we found that automatically obtained preoperative features (area under the curve [AUC] of 0.932, 95% confidence interval [CI] 0.910-0.951) outperforms Preoperative Score to Predict Postoperative Mortality (POSPOM) scores (AUC of 0.660, 95% CI 0.598-0.722), Charlson comorbidity scores (AUC of 0.742, 95% CI 0.658-0.812), and ASA physical status (AUC of 0.866, 95% CI 0.829-0.897). Including the ASA physical status with the preoperative features achieves an AUC of 0.936 (95% CI 0.917-0.955)., Conclusions: This automated score outperforms the ASA physical status score, the Charlson comorbidity score, and the POSPOM score for predicting in-hospital mortality. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period., (Copyright © 2019 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.)