Increased DNA methylation of the splicing regulator SR45 suppresses seed abortion in litchi.

The gene regulatory networks that govern seed development are complex, yet very little is known about the genes and processes that are controlled by DNA methylation. Here, we performed single-base resolution DNA methylome analysis and found that CHH methylation increased significantly throughout seed development in litchi. Based on the association analysis of differentially methylated regions and weighted gene co-expression network analysis (WGCNA), 46 genes were identified as essential DNA methylation-regulated candidate genes involved in litchi seed development, including LcSR45, a homolog of the serine/arginine-rich (SR) splicing regulator SR45. LcSR45 is predominately expressed in the funicle, embryo, and seed integument, and displayed increased CHH methylation in the promoter during seed development. Notably, silencing of LcSR45 in a seed-aborted litchi cultivar significantly improved normal seed development, whereas the ectopic expression of LcSR45 in Arabidopsis caused seed abortion. Furthermore, LcSR45-dependent alternative splicing events were found to regulate genes involved in seed development. Together, our findings demonstrate that LcSR45 is hypermethylated, and plays a detrimental role in litchi seed development, indicating a global increase in DNA methylation at this stage.

Wireless Sensor Networks for Noise Measurement and Acoustic Event Recognitions in Urban Environments.

Nowadays, urban noise emerges as a distinct threat to people's physiological and psychological health. Previous works mainly focus on the measurement and mapping of the noise by using Wireless Acoustic Sensor Networks (WASNs) and further propose some methods that can effectively reduce the noise pollution in urban environments. In addition, the research on the combination of environmental noise measurement and acoustic events recognition are rapidly progressing. In a real-life application, there still exists the challenges on the hardware design with enough computational capacity, the reduction of data amount with a reasonable method, the acoustic recognition with CNNs, and the deployment for the long-term outdoor monitoring. In this paper, we develop a novel system that utilizes the WASNs to monitor the urban noise and recognize acoustic events with a high performance. Specifically, the proposed system mainly includes the following three stages: (1) We used multiple sensor nodes that are equipped with various hardware devices and performed with assorted signal processing methods to capture noise levels and audio data; (2) the Convolutional Neural Networks (CNNs) take such captured data as inputs and classify them into different labels such as car horn, shout, crash, explosion; (3) we design a monitoring platform to visualize noise maps, acoustic event information, and noise statistics. Most importantly, we consider how to design effective sensor nodes in terms of cost, data transmission, and outdoor deployment. Experimental results demonstrate that the proposed system can measure the urban noise and recognize acoustic events with a high performance in real-life scenarios.