The combination of 18 to 69 hours of complete diaphragmatic inactivity and mechanical ventilation results in marked atrophy of human diaphragm myofibers. These findings are consistent with increased diaphragmatic proteolysis during inactivity.

Cancer susceptibility genes have been classified into two groups: gatekeepers and caretakers. Gatekeepers are genes that control cell proliferation and death, whereas caretakers are DNA repair genes whose inactivation leads to genetic instability. Abrogation of both caretaker and gatekeeper function markedly increases cancer susceptibility. Although the importance of Ku80 in DNA double-strand break repair is well established, neither Ku80 nor other components of the non-homologous end-joining pathway are known to have a caretaker role in maintaining genomic stability. Here we show that mouse cells deficient for Ku80 display a marked increase in chromosomal aberrations, including breakage, translocations and aneuploidy. Despite the observed chromosome instabilities, Ku80-/- mice have only a slightly earlier onset of cancer. Loss of p53 synergizes with Ku80 to promote tumorigenesis such that all Ku80-/- p53-/- mice succumb to disseminated pro-B-cell lymphoma before three months of age. Tumours result from a specific set of chromosomal translocations and gene amplifications involving IgH and c-Myc, reminiscent of Burkitt's lymphoma. We conclude that Ku80 is a caretaker gene that maintains the integrity of the genome by a mechanism involving the suppression of chromosomal rearrangements.

Abstract-In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. To validate the efficiency of the machine learning algorithms used in this study, we systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset for this study. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotion than negative one in beta and gamma bands; the neural patterns of neutral emotion have higher alpha responses at parietal and occipital sites; and for negative emotion, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition system shows that the neural patterns are relatively stable within and between sessions.