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Background: Long‐term breast cancer survivors are at risk for cardiotoxicity after treatment, but there is insufficient evidence to provide long‐term (~10 years) cardiovascular disease (CVD) screening recommendations. We sought to evaluate a tri‐modality CVD screening approach. Methods: This single‐arm, feasibility study enrolled 201 breast cancer patients treated ≥6 years prior without CVD at diagnosis. Patients were sub‐grouped: cardiotoxic (left‐sided) radiation (RT), cardiotoxic (anthracycline‐based) chemotherapy, both cardiotoxic chemotherapy and RT, and neither cardiotoxic treatment. Patients underwent electrocardiogram (EKG), transthoracic echocardiogram with strain (TTE with GLS), and coronary artery calcium computed tomography (CAC CT). The primary endpoint was preclinical or clinical CVD. Results: Median age was 50 (29–65) at diagnosis and 63 (37–77) at imaging; median interval was 11.5 years (6.7–14.5). Among sub‐groups, 44% had no cardiotoxic treatment, 31.5% had cardiotoxic RT, 16% had cardiotoxic chemotherapy, and 8.5% had both. Overall, 77.6% showed preclinical and/or clinical CVD and 51.5% showed clinical CVD. Per modality, rates of any CVD and clinical CVD were, respectively: 27.1%/10.0% on EKG, 50.0%/25.3% on TTE with GLS, and 50.8%/45.8% on CAC CT. No statistical difference was seen among the treatment subgroups (NS, χ2 test, p = 0.58/p = 0.15). Conclusion: This study identified a high incidence of CVD in heterogenous long‐term breast cancer survivors, most >10 years post‐treatment. Over half had clinical CVD findings warranting follow‐up and/or intervention. Each imaging test independently contributed to the detection rate. This provides early evidence that long‐term cardiac screening may be of value to a wider group of breast cancer survivors than previously recognized. [ABSTRACT FROM AUTHOR]

The recovery of freshly fallen meteorites from tracked and triangulated meteors is critical to determining their source asteroid families. Even though our ability to locate meteorite falls continues to improve, the recovery of meteorites remains a challenge due to large search areas with terrain and vegetation obscuration. To improve the efficiency of meteorite recovery, we have tested the hypothesis that meteorites can be located using machine learning techniques and an autonomous drone. To locate meteorites autonomously, a quadcopter drone first conducts a grid survey acquiring top‐down images of the strewn field from a low altitude. The drone‐acquired images are then analyzed using a machine learning classifier to identify meteorite candidates for follow‐up examination. Here, we describe a proof‐of‐concept meteorite classifier that deploys off‐line a combination of different convolution neural networks to recognize meteorites from images taken by drones in the field. The system was implemented in a conceptual drone setup and tested in the suspected strewn field of a recent meteorite fall near Walker Lake, Nevada. [ABSTRACT FROM AUTHOR]