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Circular RNAs (circRNAs) are a class of circular non-coding RNAs that play essential roles in the intricate and dynamic networks governing cell growth, development, and apoptosis. Resveratrol (RSV), a non-flavonoid polyphenol, is known to participate in follicular development and ovulation. In our previous research, we established a model using porcine ovarian granulosa cells (POGCs) treated with resveratrol, which confirmed its regulatory effects on long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) within these cells. However, the influence of resveratrol on circRNA expression has not been thoroughly investigated. To explore how resveratrol affects circRNA levels in POGCs, we designed an experiment with three groups: a control group (CON, n = 3, 0 μM RSV), a low-dose RSV group (LOW, n = 3, 50 μM RSV), and a high-dose RSV group (HIGH, n = 3, 100 μM RSV) for circRNA sequencing. We identified a total of 10,045 candidate circRNAs from POGCs treated with different concentrations of resveratrol (0, 50, and 100 μM). Differential expression analysis indicated that 96 circRNAs were significantly altered in the LOW vs. CON group, while 109 circRNAs showed significant changes in the HIGH vs. CON group. These circRNAs were notably enriched in biological processes associated with cell metabolism, apoptosis, and oxidative stress. Functional enrichment analysis of the host genes revealed their involvement in critical signaling pathways, including mTOR, AMPK, and apoptosis pathways. Additionally, we identified potential miRNA sponge candidates among the differentially expressed circRNAs, particularly novel_circ_0012954 and novel_circ_0004762, which exhibited strong connectivity within miRNA-target networks. Our findings provide valuable insights into the regulatory mechanisms of circRNAs in the context of resveratrol-induced apoptosis in POGCs, highlighting their potential as innovative therapeutic targets in reproductive biology. [ABSTRACT FROM AUTHOR]

The altitude of a moving user is important context information for mobile technologies and applications. However, with the increasing pervasiveness of smartphones and abundant mobile applications, developers and users have gradually discovered that the height is more useful than altitude in many situations. The height is often a relative value, which is the vertical distance to the ground rather than the vertical distance to sea level, and we believe that it is useful in many applications, such as localization/navigation, sport/health and tourism/travel. In this paper, we first carried out a nation-wide online survey to confirm the desirability for the height information in mobile applications, and the result is positive. Then, we proposed HiMeter, an effective and accurate approach to calculating the height of the smartphone. HiMeter makes use of a low-power barometer on the smartphone and does not require GPS or back-server support. We concentrate on the vertical moving pattern of the user and designed several novel techniques, resulting in HiMeter not needing any reference points, and the complex process of calculating the absolute altitude can be avoided. The field studies show that HiMeter can achieve an accuracy of within 5 m in 90% of cases indoors and an accuracy of 10 m in 83% of cases outdoors. Compared to the existing works, HiMeter is more accurate and practical and is more suitable for usage in many mobile applications. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things (IoT) has accelerated research in indoor navigation systems, a majority of which rely on adequate wireless signals and sources. Nonetheless, deploying such a system requires periodic site-survey, which is time consuming and labor intensive. To address this issue, in this paper we present Canoe , an indoor navigation system that considers shopping mall scenarios. In our system, we do not assume any prior knowledge, such as floor-plan or the shop locations, access point placement or power settings, historical RSS measurements or fingerprints, etc. Instead, Canoe requires only that the shop owners collect and publish RSS values at the entrances of their shops and can direct a consumer to any of these shops by comparing the observed RSS values. The locations of the consumers and the shops are estimated using maximum likelihood estimation. In doing this, the direction of the target shop relative to the current orientation of the consumer can be precisely computed, such that the direction that a consumer should move can be determined. We have conducted extensive simulations using a real-world dataset. Our experiments in a real shopping mall demonstrate that if 50% of the shops publish their RSS values, Canoe can precisely navigate a consumer within 30 s, with an error rate below 9%.