Enhancement of band edge emission of ZnO nanorods up to a factor of 120 times has been observed in the composite consisting of ZnO nanorods and TiO(2) nanoparticles, while the defect emission of ZnO nanorods is quenched to noise level. Through a detailed investigation, it is found that the large enhancement mainly arises from fluorescence resonance energy transfer between the band edge transition of ZnO nanorods and TiO(2) nanoparticles. Our finding opens up new possibilities for the creation of highly efficient solid state emitters.

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This study isolated agonists of peroxisome proliferator activated receptors (PPARs) from the green algae Chlorella sorokiniana, using a bioassay-guided purification strategy. PPARs are widely recognized as the molecular drug targets for many diseases including hyperglycemia, diabetes, obesity and cancer. Two independent bioassays were developed. The first is the scintillation proximity assay, a ligand binding assay. The other is the cell-based transcriptional activation assay which uses the Dual-Luciferase reporter system as the reporter gene under the control of the PPAR response element. Using these two assays, a PPARgamma-active fraction, CE 3-3, was obtained from C. sorokiniana extracts, which was also able to activate PPARalphamediated gene expression. To elucidate the active ingredients in the CE 3-3 fraction, GC-MS analysis was employed. The results showed that the CE 3-3 fraction consisted of at least ten fatty acids (FAs). The bioactivities of several of the individual FAs were evaluated for their PPARgamma activity and the results showed that linolenic acid and linoleic acid were the most potent FAs tested. Our studies indicate that Chlorella sorokiniana could have potential health benefits through the dual activation of PPARalpha/gamma via its unique FA constituents.

We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era. Over the years, developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-grained segmentation annotations. To address this issue, we first introduce a high-quality frame-by-frame annotated VPS dataset, named SUN-SEG, which contains 158 690 colonoscopy video frames from the well-known SUN-database. We provide additional annotation covering diverse types, i.e., attribute, object mask, boundary, scribble, and polygon. Second, we design a simple but efficient baseline, named PNS+, which consists of a global encoder, a local encoder, and normalized self-attention (NS) blocks. The global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations, which are then progressively refined by two NS blocks. Extensive experiments show that PNS+ achieves the best performance and real-time inference speed (170 fps), making it a promising solution for the VPS task. Third, we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based comparisons. Finally, we discuss several open issues and suggest possible research directions for the VPS community. Our project and dataset are publicly available at https://github.com/GewelsJI/VPS.

This paper introduces deep gradient network (DGNet), a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. Benefiting from the simple but efficient framework, DGNet outperforms existing state-of-the-art COD models by a large margin. Notably, our efficient version, DGNet-S, runs in real-time (80 fps) and achieves comparable results to the cutting-edge model JCSOD-CVPR21 with only 6.82% parameters. The application results also show that the proposed DGNet performs well in the polyp segmentation, defect detection, and transparent object segmentation tasks. The code will be made available at https://github.com/GewelsJI/DGNet.