Sudden cardiac arrest prediction via deep learning electrocardiogram analysis

Aims: Sudden cardiac arrest (SCA) is a commonly fatal event that often occurs without prior indications. To improve outcomes and enable preventative strategies, the electrocardiogram (ECG) in conjunction with deep learning was explored as a potential screening tool. Methods and results: A publicly available data set containing 10 s of 12-lead ECGs from individuals who did and did not have an SCA, information about time from ECG to arrest, and age and sex was utilized for analysis to individually predict SCA or not using deep convolution neural network models. The base model that included age and sex, ECGs within 1 day prior to arrest, and data sampled from windows of 720 ms around the R-waves from 221 individuals with SCA and 1046 controls had an area under the receiver operating characteristic curve of 0.77. With sensitivity set at 95%, base model specificity was 31%, which is not clinically applicable. Gradient-weighted class activation mapping showed that the model mostly relied on the QRS complex to make predictions. However, models with ECGs recorded between 1 day to 1 month and 1 month to 1 year prior to arrest demonstrated predictive capabilities. Conclusion: Deep learning models processing ECG data are a promising means of screening for SCA, and this method explains differences in SCAs due to age and sex. Model performance improved when ECGs were nearer in time to SCAs, although ECG data up to a year prior had predictive value. Sudden cardiac arrest prediction was more dependent upon QRS complex data compared to other ECG segments

4MOST: Project overview and information for the First Call for Proposals

We introduce the 4-metre Multi-Object Spectroscopic Telescope (4MOST), a new high-multiplex, wide-field spectroscopic survey facility under development for the four-metre-class Visible and Infrared Survey Telescope for Astronomy (VISTA) at Paranal. Its key specifications are: a large field of view (FoV) of 4.2 square degrees and a high multiplex capability, with 1624 fibres feeding two low-resolution spectrographs ($R = \lambda/\Delta\lambda \sim 6500$), and 812 fibres transferring light to the high-resolution spectrograph ($R \sim 20\,000$). After a description of the instrument and its expected performance, a short overview is given of its operational scheme and planned 4MOST Consortium science; these aspects are covered in more detail in other articles in this edition of The Messenger. Finally, the processes, schedules, and policies concerning the selection of ESO Community Surveys are presented, commencing with a singular opportunity to submit Letters of Intent for Public Surveys during the first five years of 4MOST operations

Rainfall events drive foraging choices by an urban coloniser

Using a seven-year data set of visitation of an inner city park by the Australian white ibis, we investigated whether rain events were correlated with ibis abundance in the park. The park is associated with high levels of anthropogenic food, but relatively low levels of natural food sources. For all magnitudes of rainfall tested, ibis abundance significantly decreased after a rainfall event, although stronger responses were associated with higher rainfall, with a 46% decline in ibis abundance following rainfall events of ¿60 mm. Average ibis abundance was higher during the dry, non-breeding period than during the breeding period, and variation associated with rainfall was particularly pronounced in the non-breeding period. However, the rainfall response was still evident in both periods. Results suggest that rainfall influences the ibis distribution in urban centres either by decreasing anthropogenic food supplied to the birds, forcing the birds to relocate to forage, or increasing the amount of natural food available elsewhere, or a combination of the two. Increased rainfall intensified the response by ibis, and our results demonstrate the importance of climatic processes on the behaviour of urban birds