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The FoxM1 transcription factor, a master regulator of mitotic gene expression, promotes the pathogenesis of several malignancies. However, little is known about its expression and function in gastric cancer. In the present study we determined whether FoxM1 is over-expressed in gastric cancer, and whether it is required to maintain an immortal phenotype of gastric cancer cells. The over-expression of FoxM1 was observed in 37/42 tumour specimens from patients with gastric cancer. When FoxM1 in gastric cancer cells was knocked-down, impaired clonogenicity and cellular senescence occurred independently of p53 and p16 status. FoxM1 depletion led to the down-regulation of its target genes c-MYC and Skp2, coupled with the accumulation of the CDK inhibitor p27(kip1). Importantly, the FoxM1 inhibition-mediated cellular senescence and clonogenic defect was attenuated by the abolition of p27(kip1) induction. Telomerase reverse transcriptase, the key component of telomerase essential for cellular immortalization, was also inhibited in the FoxM1-depleted cells. Taken together, the FoxM1 gene is aberrantly activated in gastric cancer and its inhibition triggers p53- and p16-independent senescence of cancer cells by regulating the expression of p27(kip1) and other targets. These findings provide mechanistic insights into the role of FoxM1 in the pathogenesis of gastric cancer, which may have diagnostic and therapeutic implications in gastric cancer.

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Correct detection and classification of ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) is of pivotal importance for an automatic external defibrillator and patient monitoring. In this paper, a VF/VT classification algorithm using a machine learning method, a support vector machine, is proposed. A total of 14 metrics were extracted from a specific window length of the electrocardiogram (ECG). A genetic algorithm was then used to select the optimal variable combinations. Three annotated public domain ECG databases (the American Heart Association Database, the Creighton University Ventricular Tachyarrhythmia Database, and the MIT-BIH Malignant Ventricular Arrhythmia Database) were used as training, test, and validation datasets. Different window sizes, varying from 1 to 10 s were tested. An accuracy (Ac) of 98.1%, sensitivity (Se) of 98.4%, and specificity (Sp) of 98.0% were obtained on the in-sample training data with 5 s-window size and two selected metrics. On the out-of-sample validation data, an Ac of 96.3% ± 3.4%, Se of 96.2% ± 2.7%, and Sp of 96.2% ± 4.6% were obtained by fivefold cross validation. The results surpass those of current reported methods.

Generating random binary sequences (BSes) is a fundamental requirement in cryptography. A BS is a sequence of N bits, and each bit has a value of 0 or 1. For securing sensors within wireless body area networks (WBANs), electrocardiogram (ECG)-based BS generation methods have been widely investigated in which interpulse intervals (IPIs) from each heartbeat cycle are processed to produce BSes. Using these IPI-based methods to generate a 128-bit BS in real time normally takes around half a minute. In order to improve the time efficiency of such methods, this paper presents an ECG multiple fiducial-points based binary sequence generation (MFBSG) algorithm. The technique of discrete wavelet transforms is employed to detect arrival time of these fiducial points, such as P, Q, R, S, and T peaks. Time intervals between them, including RR, RQ, RS, RP, and RT intervals, are then calculated based on this arrival time, and are used as ECG features to generate random BSes with low latency. According to our analysis on real ECG data, these ECG feature values exhibit the property of randomness and, thus, can be utilized to generate random BSes. Compared with the schemes that solely rely on IPIs to generate BSes, this MFBSG algorithm uses five feature values from one heart beat cycle, and can be up to five times faster than the solely IPI-based methods. So, it achieves a design goal of low latency. According to our analysis, the complexity of the algorithm is comparable to that of fast Fourier transforms. These randomly generated ECG BSes can be used as security keys for encryption or authentication in a WBAN system.

Space-based Global Navigation Satellite System Reflectometry (GNSS-R) altimetry remains an open challenge. This paper reports on space-based GNSS-R altimetry using 40-s period of intermediate frequency recording from the TechDemoSat-1 mission. This recording is unique because one GPS signal is reflected from ice. The waveforms that are used to determine path delay are generated by 1 ms coherent integration. Pseudoranges are smoothed every 0.5 s by linear models before calculating the path delay. Altimetric results are compared to DTU10 mean sea surface heights, with good agreement being obtained. The RMS difference of 4.4 m is much smaller than reported in the current literature. Very good altimetric precision of better than 1 m (0.96 m) is achieved with a spatial resolution of 3.8 km. This result validates the potential of space-based GNSS-R altimetry. Index Terms-Altimetry, global navigation satellite system reflectometry (GNSS-R), space-based GNSS-R, waveform.

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