Spider-Silk-Inspired Nanocomposite Polymers for Durable Daytime Radiative Cooling.

Passive daytime radiative cooling (PDRC) materials, that strongly reflect sunlight and emit thermal radiation to outer space, demonstrate great potential in energy-saving for sustainable development. Particularly, polymer-based PDRC materials, with advantages of easy-processing, low cost, and outstanding cooling performance, have attracted intense attention. However, just like other polymer devices (for example polymer solar cells) working under sunlight, the issue of durability related to mechanical and UV properties needs to be addressed for large-scale practical applications. Here, a spider-silk-inspired design of nanocomposite polymers with potassium titanate (K2 Ti6 O13 ) nanofiber dopants is proposed for enhancing the durability without compromising their cooling performance. The formed tough interface of nanofiber/polymer effectively disperses stress, enhancing the mechanical properties of the polymer matrix; while the K2 Ti6 O13 can absorb high-energy UV photons and transform them into less harmful heat, thereby improving the UV stabilities. Taking poly(ethylene oxide) radiative cooler as an example for demonstration, its Young's modulus and UV resistance increase by 7 and 12 times, respectively. Consequently, the solar reflectance of nanocomposite poly(ethylene oxide) is maintained as constant in a continuous aging test for 720 h under outdoor sunlight. The work provides a general strategy to simultaneously enhance both the mechanical stability and the UV durability of polymer-based PDRC materials toward large-scale applications.

Difference-Guided Representation Learning Network for Multivariate Time-Series Classification.

Multivariate time series (MTSs) are widely found in many important application fields, for example, medicine, multimedia, manufacturing, action recognition, and speech recognition. The accurate classification of MTS has become an important research topic. Traditional MTS classification methods do not explicitly model the temporal difference information of time series, which is, in fact, important and reflects the dynamic evolution information. In this article, the difference-guided representation learning network (DGRL-Net) is proposed to guide the representation learning of time series by dynamic evolution information. The DGRL-Net consists of a difference-guided layer and a multiscale convolutional layer. First, in the difference-guided layer, we propose a difference gating LSTM to model the time dependency and dynamic evolution of the time series to obtain feature representations of both raw and difference series. Then, these two representations are used as two input channels of the multiscale convolutional layer to extract multiscale information. Extensive experiments demonstrate that the proposed model outperforms state-of-the-art methods on 18 MTS benchmark datasets and achieves competitive results on two skeleton-based action recognition datasets. Furthermore, the ablation study and visualized analysis are designed to verify the effectiveness of the proposed model.

TSPAN31 suppresses cell proliferation in human cervical cancer through down-regulation of its antisense pairing with CDK4.

Natural antisense transcripts (NAT) are prevalent phenomena in the mammalian genome and play significant regulatory roles in gene expression. While new insights into NAT continue to be revealed, their exact function and their underlying mechanisms in human cancer remain largely unclear. We identified a NAT of CDK4, referred to TSPAN31, which inhibits CDK4 mRNA and protein expression in human cervical cancer by targeting the 3'-untranslated region (3'-UTR) of the CDK4 mRNA. Furthermore, silencing the expression of the TSPAN31 mRNA rescued the TSPAN31 3'-UTR- or the TSPAN31 full-length-induced decrease in CDK4 expression. Noteworthy, we discovered that TSPAN31, as a member of the tetraspanin family, suppressed cell proliferation by down-regulating its antisense pairing with CDK4 and decreasing retinoblastoma protein phosphorylation in human cervical cancer. Therefore, the results of the present study suggest that TSPAN31 may serve as a potential molecular target for the development of novel anti-cancer agents. SIGNIFICANCE OF THE STUDY: Natural antisense transcripts are widely found in the genome and play an important role in the growth and development of cells. TSPAN31 is natural antisense transcript, and CDK4 is an important gene in the regulation of the cell cycle. Therefore, TSPAN31 and CDK4 have great significance in the study of tumour therapeutic targets.