p27 shifts from CDK inhibitor to oncogene when phosphorylated by PI3K effector kinases. Here, we show that p27 is a cJun coregulator, whose assembly and chromatin association is governed by p27 phosphorylation. In breast and bladder cancer cells with high p27pT157pT198 or expressing a CDK-binding defective p27pT157pT198 phosphomimetic (p27CK−DD), cJun is activated and interacts with p27, and p27/cJun complexes localize to the nucleus. p27/cJun up-regulatesTGFB2to drive metastasis in vivo. Global analysis of p27 and cJun chromatin binding and gene expression shows that cJun recruitment to many target genes is p27 dependent, increased by p27 phosphorylation, and activates programs of epithelial–mesenchymal transformation and metastasis. Finally, human breast cancers with high p27pT157 differentially express p27/cJun-regulated genes of prognostic relevance, supporting the biological significance of the work.

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The degradation of high power GaN/InGaN blue light-emitting diodes (LEDs) was investigated by considering the electrical, optical and thermal ageing characteristics. The LED samples were stressed at the elevated temperature of 85 °C with an injection current of 350 mA. Changes in the tunnelling current and series resistance for the electrical characteristics and an initial increase followed by a gradual decrease for the optical power were observed. Variations of the thermal resistance in the chip and package were found to be 2 °C W−1 and 0.3 °C W−1, respectively. The responsible factors were proposed to be: (a) the dopant activation and changes of defects in the chip level; (b) the yellowing of the optical lens and structural degradations such as generating voids or delaminations in the package level. The changes in the electrical, optical and thermal characteristics were found to depend on and affect each other. The internal relationship for the characteristics of the three aspects was explained.

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This paper aims to investigate the robust and distinguishable pattern of heart rate variability (HRV) signals, acquired from wearable electrocardiogram (ECG) or photoplethysmogram (PPG) sensors, for driver drowsiness detection. As wearable sensors are so vulnerable to slight movement, they often produce more noise in signals. Thus, from noisy HRV signals, we need to find good traits that differentiate well between drowsy and awake states. To this end, we explored three types of recurrence plots (RPs) generated from the R–R intervals (RRIs) of heartbeats: Bin-RP, Cont-RP, and ReLU-RP. Here Bin-RP is a binary recurrence plot, Cont-RP is a continuous recurrence plot, and ReLU-RP is a thresholded recurrence plot obtained by filtering Cont-RP with a modified rectified linear unit (ReLU) function. By utilizing each of these RPs as input features to a convolutional neural network (CNN), we examined their usefulness for drowsy/awake classification. For experiments, we collected RRIs at drowsy and awake conditions with an ECG sensor of the Polar H7 strap and a PPG sensor of the Microsoft (MS) band 2 in a virtual driving environment. The results showed that ReLU-RP is the most distinct and reliable pattern for drowsiness detection, regardless of sensor types (i.e., ECG or PPG). In particular, the ReLU-RP based CNN models showed their superiority to other conventional models, providing approximately 6–17% better accuracy for ECG and 4–14% for PPG in drowsy/awake classification.

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