Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes

AbstractObjectiveWe sought to assess whether genetic risk factors for atrial fibrillation can explain cardioembolic stroke risk.MethodsWe evaluated genetic correlations between a prior genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously-validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors.ResultsWe observed strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson’s r=0.77 and 0.76, respectively, across SNPs with p &lt; 4.4 × 10−4 in the prior AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio (OR) per standard deviation (sd) = 1.40, p = 1.45×10−48), explaining ∼20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per sd = 1.07, p = 0.004), but no other primary stroke subtypes (all p &gt; 0.1).ConclusionsGenetic risk for AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.</jats:sec

Offshore occurrence of a migratory bat, pipistrellus nathusii, depends on seasonality and weather conditions

Bats regularly migrate over the North Sea, but information on the environmental conditions when this occurs is scarce. Detailed information is urgently needed on the conditions under which bats can be expected offshore, as the number of offshore windfarms that can cause fatalities amongst bats in the North Sea is increasing rapidly. We performed ultrasonic acoustic monitoring at multiple nearshore locations at sea between 2012 and 2016 for, in total, 480 monitoring nights. We modelled the offshore occurrence of Nathusius’ pipistrelle in autumn as a function of weather conditions, seasonality, and the lunar cycle using a generalized additive mixed model (GAMM). We investigated which covariates are important using backward selection based on a likelihood ratio test. Our model showed that important explanatory variables for the offshore occurrence of Nathusius’ pipistrelle are seasonality (night in year), wind speed, wind direction, and temperature. The species’ migration is strongest in early September, with east-northeasterly tailwinds, wind speeds 15◦C. Lunar cycle, cloud cover, atmospheric pressure, atmospheric pressure change, rain, and visibility were excluded during the model selection. These results provide valuable input to reduce bat fatalities in offshore wind farms by taking mitigation measures

Marine mammal surveys in Dutch North Sea waters in 2019

Aerial surveys to estimate the abundance of Harbour Porpoise Phocoena phocoena were conducted on the Dutch Continental Shelf in summer 2019. These surveys followed predetermined track lines in four areas: A - Dogger Bank, B - Offshore, C - Frisian Front & D - Delta. Between 16 July and 4 August the entire Dutch Continental Shelf (DCS) was surveyed. Marine mammals were assessed using line transect distance sampling methods. Density and abundance estimates were calculated. In total, 150 sightings of 189 individual Harbour Porpoises were collected. Porpoise densities varied between 0.54-1.76 animals/km² in the areas A-D. The overall density was 0.66 animals/km². The lowest density (0.46 animals/km²) was recorded in area A – Dogger Bank. The densities in the other areas were in the same order of magnitude, ranging between 0.68-071 animals/km². In summer 2019 the total number of Harbour Porpoises on the Dutch Continental Shelf (areas A-D) was estimated at 38,911 individuals (CI = 20,791-76,822). This estimates falls in the range of abundance estimates since 2010, with a minimum of 25,998 (CI = 13,988 – 53,623 in 2010) and a maximum of 76,773 (CI = 43,414-154,265 in 2014) individuals. The confidence intervals of the abundance estimates overlap, indicating no statistically significant differences between the years. The time series, however, is relatively short to measure trends. These abundance estimates show that up to a fifth of the North Sea population, estimated at 345,000-361,000 individuals, has been present on the Dutch Continental Shelf during the summer surveys in 2010-2019. The results of these aerial surveys will feed into the OSPAR MSFD indicator on abundance and distribution of marine mammals. In total 26 sightings of other marine mammal species than Harbour Porpoises were recorded. These comprised 22 sightings of seals (Grey Seal Halichoerus grypus and Harbour Seal Phoca vitulina). The majority of the seals was observed in coastal waters off the Wadden Isles. Three single Minke Whales Balaenoptera acutorostrata were seen (feeding) in area A – Dogger Bank and B – Offshore, with another one sighted off effort in the same area. One sighting of a pod of two White-beaked Dolphins Lagenorhynchus albirostris was made in area B – Offshore. This research is part of the BO-project ‘monitoring bruinvis’

Quantifying harbour porpoise foraging behaviour in CPOD data

Harbour porpoises (Phocoena phocoena) are regularly monitored to assess how they are impacted by the construction and operation of offshore wind farms. A suitable method to do this is passive acoustic monitoring (PAM) by stationary hydrophones, for example CPODs. These devices provide information on echolocation click activity, which can then be analysed. Prey occurrence is considered one of the main drivers in porpoise distribution and successful feeding is vital to the fitness and survival of individual porpoises. Information on foraging behavior, however, is difficult to obtain in the field, in particular as animals feed under water. Harbour porpoise use narrow band high frequency signals in a sequence of clicks (called click trains) for echolocation, communication and foraging. The different behaviors are characterised by the modulation in time lag between clicks (inter-click interval). Using CPOD data collected in Dutch water during and after pile driving noise exposure, the present study first investigated different data processing methods for the quantification of foraging behavior. The results indicate that: (1) a click-based classification provides the best results (as opposed to using click trains), (2) foraging events could be detected in sufficient numbers to reveal patterns over time, such as correlation with pile driving activities