BACKGROUND Dilated cardiomyopathy and hypertrophic cardiomyopathy arise from mutations in many genes. TTN, the gene encoding the sarcomere protein titin, has been insufficiently analyzed for cardiomyopathy mutations because of its enormous size. METHODS We analyzed TTN in 312 subjects with dilated cardiomyopathy, 231 subjects with hyper-trophic cardiomyopathy, and 249 controls by using next-generation or dideoxy sequencing. We evaluated deleterious variants for cosegregation in families and assessed clinical characteristics. RESULTS We identified 72 unique mutations (25 nonsense, 23 frameshift, 23 splicing, and 1 large tandem insertion) that altered full-length titin. Among subjects studied by means of next-generation sequencing, the frequency of TTN mutations was significantly higher among subjects with dilated cardiomyopathy (54 of 203 [27%]) than among subjects with hypertrophic cardiomyopathy (3 of 231 [1%], P = 3×10−16) or controls (7 of 249 [3%], P = 9×10−14). TTN mutations cosegregated with dilated cardiomyopathy in families (combined lod score, 11.1) with high (>95%) observed penetrance after the age of 40 years. Mutations associated with dilated cardiomyopathy were overrepresented in the titin A-band but were absent from the Z-disk and M-band regions of titin (P≤0.01 for all comparisons). Overall, the rates of cardiac outcomes were similar in subjects with and those without TTN mutations, but adverse events occurred earlier in male mutation carriers than in female carriers (P = 4×10−5). CONCLUSIONS TTN truncating mutations are a common cause of dilated cardiomyopathy, occurring in approximately 25% of familial cases of idiopathic dilated cardiomyopathy and in 18% of sporadic cases. Incorporation of sequencing approaches that detect TTN truncations into genetic testing for dilated cardiomyopathy should substantially increase test sensitivity, thereby allowing earlier diagnosis and therapeutic intervention for many patients with dilated cardiomyopathy. Defining the functional effects of TTN truncating mutations should improve our understanding of the pathophysiology of dilated cardiomyopathy. (Funded by the Howard Hughes Medical Institute and others.)

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Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

Abstract-Visual surveillance in dynamic scenes, especially for humans and vehicles, is currently one of the most active research topics in computer vision. It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors, and interactive surveillance using multiple cameras, etc. In general, the processing framework of visual surveillance in dynamic scenes includes the following stages: modeling of environments, detection of motion, classification of moving objects, tracking, understanding and description of behaviors, human identification, and fusion of data from multiple cameras. We review recent developments and general strategies of all these stages. Finally, we analyze possible research directions, e.g., occlusion handling, a combination of twoand three-dimensional tracking, a combination of motion analysis and biometrics, anomaly detection and behavior prediction, content-based retrieval of surveillance videos, behavior understanding and natural language description, fusion of information from multiple sensors, and remote surveillance.Index Terms-Behavior understanding and description, fusion of data from multiple cameras, motion detection, personal identification, tracking, visual surveillance.