Background-Titin is a giant elastic protein that spans the half-sarcomere from Z-disk to Mband. It acts as a molecular spring and mechanosensor and has been linked to striated muscle disease. The pathways that govern titin dependent cardiac growth and contribute to disease are diverse and difficult to dissect. Methods-To study titin deficiency versus dysfunction, we generated and compared striated muscle specific knockouts with progressive postnatal loss of the complete titin protein by removing exon 2 (E2-KO) or an M-band truncation that eliminates proper sarcomeric integration but retains all other functional domains (M1/2-KO). We evaluated cardiac function, cardiomyocyte mechanics, and the molecular basis of the phenotype. Results-Skeletal muscle atrophy with reduced strength, severe sarcomere disassembly, and lethality from 2 weeks of age were shared between the models. Cardiac phenotypes differed considerably: loss of titin leads to dilated cardiomyopathy (DCM) with combined systolic and diastolic dysfunction-the absence of M-band titin to cardiac atrophy and preserved function. The elastic properties of M-1/2-KO cardiomyocytes are maintained, while passive stiffness is reduced in the E2-KO. In both KOs, we find an increased stress response and increased expression of proteins linked to titin-based mechanotransduction (CryAB, ANKRD1, MLP, FHLs, p42, Camk2d, p62 and Nbr1). Among them, FHL2, and the M-band signaling proteins p62 and Nbr1 are exclusively upregulated in the E2-KO suggesting a role in the differential pathology of titin truncation versus deficiency of the full-length protein. The differential stress response is consistent with truncated titin contributing to the mechanical properties in M1/2-KOs, while low titin levels in E2-KOs lead to reduced titin-based stiffness and increased strain on the remaining titin molecules.
Carefully managing the presentation of self via technology is a core practice on all modern social media platforms. Recently, selfies have emerged as a new, pervasive genre of identity performance. In many ways unique, selfies bring us full-circle to Goffman — blending the online and offline selves together. In this paper, we take an empirical, Goffman-inspired look at the phenomenon of selfies. We report a large-scale, mixed-method analysis of the categories in which selfies appear on Instagram — an online community comprising over 400M people. Applying computer vision and network analysis techniques to 2.5M selfies, we present a typology of emergent selfie categories which represent emphasized identity statements. To the best of our knowledge, this is the first large-scale, empirical research on selfies. We conclude, contrary to common portrayals in the press, that selfies are really quite ordinary: they project identity signals such as wealth, health and physical attractiveness common to many online media, and to offline life.
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