Automated detection and tracking of marine mammals : a novel sonar tool for monitoring effects of marine industry

Funding: The work was funded under the Scottish Government Demonstration Strategy (Project no. USA/010/14)and as part of the Department of Energy and Climate Change’s Offshore Energy Strategic Environmental Assessment programme, with additional resources from the Natural Environment Research Council (grant numbers: NE/R014639/1 and SMRU1001).1. Many marine industries may pose acute risks to marine wildlife. For example, tidal turbines have the potential to injure or kill marine mammals through collisions with turbine blades. However, the quantification of collision risk is currently limited by a lack of suitable technologies to collect long‐term data on marine mammal behaviour around tidal turbines. 2. Sonar provides a potential means of tracking marine mammals around tidal turbines. However, its effectiveness for long‐term data collection is hindered by the large data volumes and the need for manual validation of detections. Therefore, the aim here was to develop and test automated classification algorithms for marine mammals in sonar data. 3. Data on the movements of harbour seals were collected in a tidally energetic environment using a high‐frequency multibeam sonar on a custom designed seabed‐mounted platform. The study area was monitored by observers to provide visual validation of seals and other targets detected by the sonar. 4. Sixty‐five confirmed seals and 96 other targets were detected by the sonar. Movement and shape parameters associated with each target were extracted and used to develop a series of classification algorithms. Kernel support vector machines were used to classify targets (seal vs. nonseal) and cross‐validation analyses were carried out to quantify classifier efficiency. 5. The best‐fit kernel support vector machine correctly classified all the confirmed seals but misclassified a small percentage of non‐seal targets (~8%) as seals. Shape and non‐spectral movement parameters were considered to be the most important in achieving successful classification. 6. Results indicate that sonar is an effective method for detecting and tracking seals in tidal environments, and the automated classification approach developed here provides a key tool that could be applied to collecting long‐term behavioural data around anthropogenic activities such as tidal turbines.PostprintPeer reviewe

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Automated detection and tracking of marine mammals:a novel sonar tool for monitoring effects of marine industry

1. Many marine industries may pose acute risks to marine wildlife. For example, tidal turbines have the potential to injure or kill marine mammals through collisions with turbine blades. However, the quantification of collision risk is currently limited by a lack of suitable technologies to collect long‐term data on marine mammal behaviour around tidal turbines.2. Sonar provides a potential means of tracking marine mammals around tidal turbines. However, its effectiveness for long‐term data collection is hindered by the large data volumes and the need for manual validation of detections. Therefore, the aim here was to develop and test automated classification algorithms for marine mammals in sonar data.3. Data on the movements of harbour seals were collected in a tidally energetic environment using a high‐frequency multibeam sonar on a custom designed seabed‐mounted platform. The study area was monitored by observers to provide visual validation of seals and other targets detected by the sonar.4. Sixty‐five confirmed seals and 96 other targets were detected by the sonar. Movement and shape parameters associated with each target were extracted and used to develop a series of classification algorithms. Kernel support vector machines were used to classify targets (seal vs. nonseal) and cross‐validation analyses were carried out to quantify classifier efficiency.5. The best‐fit kernel support vector machine correctly classified all the confirmed seals but misclassified a small percentage of non‐seal targets (~8%) as seals. Shape and non‐spectral movement parameters were considered to be the most important in achieving successful classification.6. Results indicate that sonar is an effective method for detecting and tracking seals in tidal environments, and the automated classification approach developed here provides a key tool that could be applied to collecting long‐term behavioural data around anthropogenic activities such as tidal turbines