The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" shortterm tracking in RGB, (iii) VOT-LT2019 focused on longterm tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website 1 .

hest radiography, one of the most common diagnostic imaging tests in medicine, is used for screening, diagnostic work-ups, and monitoring of various thoracic diseases (1,2). One of its major objectives is detection of pulmonary nodules because pulmonary nodules are often the initial radiologic manifestation of lung cancers (1,2). However, to date, pulmonary nodule detection on chest radiographs has not been completely satisfactory, with a reported sensitivity ranging between 36%-84%, varying widely according to the tumor size and study population (2-6). Indeed, chest radiography has been shown to be prone to many reading errors with low interobserver and intraobserver agreements because of its limited spatial resolution, noise from overlapping anatomic structures, and the variable perceptual ability of radiologists. Recent work shows that 19%-26% of lung cancers visible on chest radiographs were in fact missed at their first readings (6,7). Of course, hindsight is always perfect when one knows where to look. For this reason, there has been increasing dependency on chest CT images over chest radiographs in pulmonary nodule detection. However, even low-dose CT scans require approximately 50-100 times higher radiation dose than single-view chest radiographic examinations (8,9)

Key Points Question Can a deep learning–based algorithm accurately discriminate abnormal chest radiograph results showing major thoracic diseases from normal chest radiograph results? Findings In this diagnostic study of 54 221 chest radiographs with normal findings and 35 613 with abnormal findings, the deep learning–based algorithm for discrimination of chest radiographs with pulmonary malignant neoplasms, active tuberculosis, pneumonia, or pneumothorax demonstrated excellent and consistent performance throughout 5 independent data sets. The algorithm outperformed physicians, including radiologists, and enhanced physician performance when used as a second reader. Meaning A deep learning–based algorithm may help improve diagnostic accuracy in reading chest radiographs and assist in prioritizing chest radiographs, thereby increasing workflow efficacy.

Metabolic dysfunction is a primary feature of Werner syndrome (WS), a human premature aging disease caused by mutations in the gene encoding the Werner (WRN) DNA helicase. WS patients exhibit severe metabolic phenotypes, but the underlying mechanisms are not understood, and whether the metabolic deficit can be targeted for therapeutic intervention has not been determined. Here we report impaired mitophagy and depletion of NAD+, a fundamental ubiquitous molecule, in WS patient samples and WS invertebrate models. WRN regulates transcription of a key NAD+ biosynthetic enzyme nicotinamide nucleotide adenylyltransferase 1 (NMNAT1). NAD+ repletion restores NAD+ metabolic profiles and improves mitochondrial quality through DCT-1 and ULK-1-dependent mitophagy. At the organismal level, NAD+ repletion remarkably extends lifespan and delays accelerated aging, including stem cell dysfunction, in Caenorhabditis elegans and Drosophila melanogaster models of WS. Our findings suggest that accelerated aging in WS is mediated by impaired mitochondrial function and mitophagy, and that bolstering cellular NAD+ levels counteracts WS phenotypes.