Physiological constraints and energetic costs of diving behaviour in marine mammals : a review of studies using trained Steller sea lions diving in the open ocean

The research was funded through a number of sources, including grants provided by the Natural Sciences and Engineering Research Council (Canada) and from the US National Oceanic and Atmospheric Administration to the North Pacific Universities Marine Mammal Research Consortium through the North Pacific Marine Science Foundation.Marine mammals are characterized as having physiological specializations that maximize the use of oxygen stores to prolong time spent under water. However, it has been difficult to undertake the requisite controlled studies to determine the physiological limitations and trade-offs that marine mammals face while diving in the wild under varying environmental and nutritional conditions. For the past decade, Steller sea lions (Eumetopias jubatus) trained to swim and dive in the open ocean away from the physical confines of pools participated in studies that investigated the interactions between diving behaviour, energetic costs, physiological constraints, and prey availability. Many of these studies measured the cost of diving to understand how it varies with behaviour and environmental and physiological conditions. Collectively, these studies show that the type of diving (dive bouts or single dives), the level of underwater activity, the depth and duration of dives, and the nutritional status and physical condition of the animal affect the cost of diving and foraging. They show that dive depth, dive and surface duration, and the type of dive result in physiological adjustments (heart rate, gas exchange) that may be independent of energy expenditure. They also demonstrate that changes in prey abundance and nutritional status cause sea lions to alter the balance between time spent at the surface acquiring oxygen (and offloading CO2 and other metabolic by-products) and time spent at depth acquiring prey. These new insights into the physiological basis of diving behaviour further our understanding of the potential scope for behavioural responses of marine mammals to environmental changes, the energetic significance of these adjustments, and the consequences of approaching physiological limits.PostprintPeer reviewe

Harbour porpoises exhibit localized evasion of a tidal turbine

Funding: Scottish Government (Grant Number(s): Marine Mammal Scientific Support Program MMSS/002/); Natural Environment Research Council (Grant Number(s): NE/R014639/1, NE/R015007/1).1. Tidal energy generators have the potential to injure or kill marine animals, including small cetaceans, through collisions with moving turbine parts. Information on the fine scale behaviour of animals close to operational turbines is required to inform regulators of the likely impact of these new technologies. 2. Harbour porpoise movements were monitored in three dimensions around a tidal turbine for 451 days between October 2017 and April 2019 with a 12-channel hydrophone array. 3. Echolocation clicks from 344 porpoise events were localized close to the turbine. The data show that porpoises effectively avoid the turbine rotors, with only a single animal clearly passing through the rotor swept area while the rotors were stationary, and none passing through while rotating. 4. The results indicate that the risk of collisions between the tidal turbine and porpoises is low; this has important implications for the potential effects and the sustainable development of the tidal energy industry.Publisher PDFPeer reviewe

Automated detection and tracking of marine mammals in the vicinity of tidal turbines using multibeam sonar

Funding: The monitoring platform was developed with funds from the Natural Environment Research Council (Grant Nos. NE/R015007/1 and NE/R014639/1). Software development and data analysis was funded by the Scottish Government as part of the Marine Mammal Scientific Support Program (Grant No. MMSS/002/15). Umbilical cables to the turbine infrastructure were funded and developed by SIMEC Atlantis.Understanding how marine animals behave around tidal turbines is essential if we are to quantify how individuals and populations may be affected by the installation of these devices in the coming decades. Our particular interest is in collision risk, and how this may be affected by the fine-scale behaviour of seals and small cetacean species around devices. We report on a study in which multibeam sonar data were collected close to an operational tidal turbine in Scotland continuously over a twelve-month period. The sonars provide high-resolution (a few cm) data over a 120° angle out to a range of 55 m at a rate of 10 frames per second. We describe a system which uses automatic computer algorithms to detect potential targets of interest, verified by human analysts using a sophisticated computer user interface to confirm detections and assign target species. To date, we have identified 359 tracks of marine mammals in the data, as well as several thousand tracks from fish and diving birds. These are currently being parameterised to study how these species react to the moving turbine rotors, and the data are now being used to explore the development of improved automated detection and classification algorithms.Publisher PDFPeer reviewe

Synergistic drug combinations from electronic health records and gene expression.

ObjectiveUsing electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.MethodWe applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis.ResultsFrom EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence.ConclusionsThis is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing

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

Association between Residences in U.S. Northern Latitudes and Rheumatoid Arthritis: A Spatial Analysis of the Nurses’ Health Study

Background: The etiology of rheumatoid arthritis (RA) remains largely unknown, although epidemiologic studies suggest genetic and environmental factors may play a role. Geographic variation in incident RA has been observed at the regional level. Objective: Spatial analyses are a useful tool for confirming existing exposure hypotheses or generating new ones. To further explore the association between location and RA risk, we analyzed individual-level data from U.S. women in the Nurses’ Health Study, a nationwide cohort study. Methods: Participants included 461 incident RA cases and 9,220 controls with geocoded addresses; participants were followed from 1988 to 2002. We examined spatial variation using addresses at baseline in 1988 and at the time of case diagnosis or the censoring of controls. Generalized additive models (GAMs) were used to predict a continuous risk surface by smoothing on longitude and latitude while adjusting for known risk factors. Permutation tests were conducted to evaluate the overall importance of location and to identify, within the entire study area, those locations of statistically significant risk. Results: A statistically significant area of increased RA risk was identified in the northeast United States (p-value = 0.034). Risk was generally higher at northern latitudes, and it increased slightly when we used the nurses’ 1988 locations compared with those at the time of diagnosis or censoring. Crude and adjusted models produced similar results. Conclusions: Spatial analyses suggest women living in higher latitudes may be at greater risk for RA. Further, RA risk may be greater for locations that occur earlier in residential histories. These results illustrate the usefulness of GAM methods in generating hypotheses for future investigation and supporting existing hypotheses

Comparing spatial patterns of marine vessels between vessel-tracking data and satellite imagery

Monitoring marine use is essential to effective management but is extremely challenging, particularly where capacity and resources are limited. To overcome these limitations, satellite imagery has emerged as a promising tool for monitoring marine vessel activities that are difficult to observe through publicly available vessel-tracking data. However, the broader use of satellite imagery is hindered by the lack of a clear understanding of where and when it would bring novel information to existing vessel-tracking data. Here, we outline an analytical framework to (1) automatically detect marine vessels in optical satellite imagery using deep learning and (2) statistically contrast geospatial distributions of vessels with the vessel-tracking data. As a proof of concept, we applied our framework to the coastal regions of Peru, where vessels without the Automatic Information System (AIS) are prevalent. Quantifying differences in spatial information between disparate datasets—satellite imagery and vessel-tracking data—offers insight into the biases of each dataset and the potential for additional knowledge through data integration. Our study lays the foundation for understanding how satellite imagery can complement existing vessel-tracking data to improve marine oversight and due diligence

Circulating Growth Differentiation Factor 15 Is Increased Preceding Preeclampsia Diagnosis: Implications as a Disease Biomarker

Background We investigated the biomarker potential of growth differentiation factor 15 (GDF-15), a stress response protein highly expressed in placenta, to predict preeclampsia. Methods and Results In 2 prospective cohorts (cohort 1: 960 controls, 39 women who developed preeclampsia; cohort 2: 950 controls, 41 developed preeclampsia), plasma concentrations of GDF-15 at 36 weeks' gestation were significantly increased among those who developed preeclampsia (P<0.001), area under the receiver operating characteristic curves (AUC) of 0.66 and 0.71, respectively. In cohort 2 a ratio of sFlt-1/PlGF (a clinical biomarker for preeclampsia) had a sensitivity of 61.0% at 83.2% specificity to predict those who will develop preeclampsia (AUC of 0.79). A ratio of GDF-15×sFlt-1/PlGF yielded a sensitivity of 68.3% at 83.2% specificity (AUC of 0.82). GDF-15 was consistently elevated across a number of international cohorts: levels were higher in placenta and blood from women delivering <34 weeks' gestation due to preterm preeclampsia in Melbourne, Australia; and in the blood at 26 to 32 weeks' gestation among 57 women attending the Manchester Antenatal Vascular Service (MAViS, UK) who developed preeclampsia (P=0.0002), compared with 176 controls. In the Preeclampsia Obstetric adVerse Events biobank (PROVE, South Africa), plasma GDF-15 was significantly increased in women with preeclampsia with severe features (P=0.02; n=14) compared to controls (n=14). Conclusions We conclude circulating GDF-15 is elevated among women more likely to develop preeclampsia or diagnosed with the condition. It may have value as a clinical biomarker, including the potential to improve the sensitivity of sFlt-1/PlGF ratio