Fabrication of highly sensitive and uniform Ag/PS/PDMS SERS substrate and its application forin-situdetection.

In this study, we developed a flexible and transparent silver/polystyrene/polydimethylsiloxane (Ag/PS/PDMS) substrate with both high density of hot spots and satisfactory uniformity using a cost-effective approach. Via template-guided self-assembly, PS beads were arranged regularly in nanobowls of a square array on PDMS, whose surface structure was transferred from a commercial complementary metal oxide semiconductor chip. Roughness was introduced onto the PS bead surface by nitrogen plasma treatment, followed by sputtering of Ag which generated many hot spots. Differential roughness on the PS bead surface greatly influenced the morphology of the Ag/PS/PDMS substrate. A meat-ball like surface structure was formed with a plasma etching time of 5 min, whose growth mechanism was proposed based on the scanning electron microscope analysis. The high sensitivity and desirable uniformity of the meat-ball like Ag/PS/PDMS substrate were demonstrated by using crystal violet as a Raman reporter, exhibiting an enhancement factor of 2.7 × 107and a relative standard deviation of 5.04%. Thiram of a lower concentration than the maximum residue limit on the cucumber surface could easily be detectedin situby the proposed substrate, demonstrating its great potential forin-situfood safety analysis.

Context-aware Sampling of Large Networks via Graph Representation Learning.

Numerous sampling strategies have been proposed to simplify large-scale networks for highly readable visualizations. It is of great challenge to preserve contextual structures formed by nodes and edges with tight relationships in a sampled graph, because they are easily overlooked during the process of sampling due to their irregular distribution and immunity to scale. In this paper, a new graph sampling method is proposed oriented to the preservation of contextual structures. We first utilize a graph representation learning (GRL) model to transform nodes into vectors so that the contextual structures in a network can be effectively extracted and organized. Then, we propose a multi-objective blue noise sampling model to select a subset of nodes in the vectorized space to preserve contextual structures with the retention of relative data and cluster densities in addition to those features of significance, such as bridging nodes and graph connections. We also design a set of visual interfaces enabling users to interactively conduct context-aware sampling, visually compare results with various sampling strategies, and deeply explore large networks. Case studies and quantitative comparisons based on real-world datasets have demonstrated the effectiveness of our method in the abstraction and exploration of large networks.

Identification of differentially expressed genes that potentially confer pest resistance in transgenic ChIFN-γ tobacco.

Chicken interferon-γ (ChIFN-γ) is both an inhibitor of viral replication and a regulator of numerous immunological functions. However, since little is known about the mechanisms underlying the insect-resistance of transgenic ChIFN-γ, a transgenic ChIFN-γ tobacco line was employed in the present study to explore this mechanism. A cDNA microarray (with 43,760 unigenes) was used to analyze the gene expression profiles of transgenic and wild-type (WT) tobacco leaves at two different growth stages. Compared with the WT, 1529 and 405 expressed sequence tags were significantly up- or downregulated on days 119 and 147, respectively. The differentially expressed genes (DEGs) are involved in metabolic regulation, cell division and differentiation, material synthesis and transport, signal transduction, and protein synthesis and degradation. Candidate genes that may increase cell density, thicken cell walls, promote secondary metabolite synthesis, and mediate plant hormone-induced resistance responses were used to identify the ChIFN-γ-mediated insect-resistance mechanisms. The insect-resistance of transgenic ChIFN-γ tobacco possibly involves unknown signaling pathways, which may directly or indirectly affect DEG expression-mediating genes. The degree of pest resistance increased as the plants grew. Three genes likely to be related to jasmonic acid- or salicylic acid-dependent plant defense responses, including CAF 1, Cop 8/CSN, and HD, are implicated in the insect-resistance of the transgenic plants. The mechanism of transgenic ChIFN-γ tobacco resistance also involves RPS20 and other genes that induce microRNA-based gene regulation. The ChIFN-γ-mediated DGEs contribute to insect-resistance in transgenic ChIFN-γ tobacco, which provides new insight into the role of ChIFN-γ.