Background: Osteoarthritis (OA) affects about 40% of people older than 40 years of age, and the mechanism is not well understood. Long non-coding RNA (lncRNA) CAIF is a recently identified critical player in myocardial infarction, while its role in other human diseases is unclear. The present study aimed to investigate the role of CAIF in OA. Material/Methods: Levels of CAIF in synovial fluid of OA patients (n=60) and healthy controls (n=60) were measuring by performing quantitative real-time polymerase chain reaction (qRT-PCR). MiR-1246 and interleukin (IL)-6 levels in synovial fluid were measured by performing qRT-PCR and enzyme-linked immunosorbent assay (ELISA), respectively. Cell apoptosis analysis was performed after CHON-001 cells were treated with 500 mg/mL lipopolysaccharide (LPS) for 24 hours. Results: We found that CAIF in synovial fluid was downregulated in OA patients and inversely correlated with miR-1246 and IL-6. Downregulated CAIF distinguished OA patients from healthy controls. In cells of chondrogenic cell line CHON-001, CAIF overexpression mediated the inhibited expression of miR-1246 and secretion of IL-6, while miR-1246 overexpression reduced the effects of CAIF overexpression on IL-6 secretion. In addition, CAIF overexpression inhibited the apoptosis of CHON-001 cells under LPS treatment, while miR-1246 overexpression attenuated the effects of CAIF overexpression. Conclusions: Therefore, CAIF may downregulate miR-1246 to improve OA.

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Abstract-With the huge amount of ubiquitous multimedia data transmitted in nowadays Internet, the use of packet sampling for traffic measurements has become widely employed for network operators. In this paper, we present an adaptive packet sampling technique from the classification perspective, the main sampling principle of which is to select as many packets with low occurrence rate as possible based on two useful features for multimedia traffic: Packet Size (PS) and Packet Inter Arrival Time (IAT). We build a model of the ideal packet sampling technique for classifying multimedia traffic, which adjusts adaptively the sampling probability of selecting packets according to PS and IAT predicted simultaneously by multi-output support vector regression, and define general indexes for evaluating the sampling performance of the proposed approach. We compare our approach with other sampling methods and evaluate their impact on the performance of traffic classification using two machine learning methods with real multimedia traffic data. The experimental results show that this approach has good sampling performance and is able to enhance the performance of the traffic classification methods.

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Abstract. The basic law and method of Fourier transformation and Hilbert Huang transformation is introduced. Taking the obtained dynamic EEG data under +Gz acceleration as an example, EEG change feature under +Gz acceleration is analyzed ,and Fourier transformation spectrum graph and Hilbert Huang transformation spectrum graph are compared, then the time-frequency characteristics of the two methods are made analysis and comparison.