AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing

Two factors have proven to be very important to the performance of semantic segmentation models: global context and multi-level semantics. However, generating features that capture both factors always leads to high computational complexity, which is problematic in real-time scenarios. In this paper, we propose a new model, called Attention-Augmented Network (AttaNet), to capture both global context and multilevel semantics while keeping the efficiency high. AttaNet consists of two primary modules: Strip Attention Module (SAM) and Attention Fusion Module (AFM). Viewing that in challenging images with low segmentation accuracy, there are a significantly larger amount of vertical strip areas than horizontal ones, SAM utilizes a striping operation to reduce the complexity of encoding global context in the vertical direction drastically while keeping most of contextual information, compared to the non-local approaches. Moreover, AFM follows a cross-level aggregation strategy to limit the computation, and adopts an attention strategy to weight the importance of different levels of features at each pixel when fusing them, obtaining an efficient multi-level representation. We have conducted extensive experiments on two semantic segmentation benchmarks, and our network achieves different levels of speed/accuracy trade-offs on Cityscapes, e.g., 71 FPS/79.9% mIoU, 130 FPS/78.5% mIoU, and 180 FPS/70.1% mIoU, and leading performance on ADE20K as well.Comment: AAAI 202

Locally-Enriched Cross-Reconstruction for Few-Shot Fine-Grained Image Classification

Few-shot fine-grained image classification has attracted considerable attention in recent years for its realistic setting to imitate how humans conduct recognition tasks. Metric-based few-shot classifiers have achieved high accuracies. However, their metric function usually requires two arguments of vectors, while transforming or reshaping three-dimensional feature maps to vectors can result in loss of spatial information. Image reconstruction is thus involved to retain more appearance details: the test images are reconstructed by different classes and then classified to the one with the smallest reconstruction error. However, discriminative local information, vital to distinguish sub-categories in fine-grained images with high similarities, is not well elaborated when only the base features from a usual embedding module are adopted for reconstruction. Hence, we propose the novel local content-enriched cross-reconstruction network (LCCRN) for few-shot fine-grained classification. In LCCRN, we design two new modules: the local content-enriched module (LCEM) to learn the discriminative local features, and the cross-reconstruction module (CRM) to fully engage the local features with the appearance details obtained from a separate embedding module. The classification score is calculated based on the weighted sum of reconstruction errors of the cross-reconstruction tasks, with weights learnt from the training process. Extensive experiments on four fine-grained datasets showcase the superior classification performance of LCCRN compared with the state-of-the-art few-shot classification methods. Codes are available at: https://github.com/lutsong/LCCRN

Construction and prokaryotic expression of the fusion gene PRRSV GP5 and Mycobacterium bovis Hsp70

Porcine reproductive and respiratory syndrome (PRRS) is an economically important swine disease that has devastated the swine industry worldwide. Vaccination with live attenuated vaccine or inactivated vaccine is the main treatment to control PRRS. However, the disadvantages such as virulence resumption of the attenuated vaccine and low immunogenicity of the inactivated vaccine call for a more efficient and safer genetically engineered vaccine. In this study, the structural protein GP5 of the PRRS virus (PRRSV), one of the major protective antigens which stimulates a protective immune response was selected to develop a genetically engineered subunit vaccine. In order to promote the immune reaction of the host to GP5, heat shock protein 70 (Hsp70) was selected as immuno-adjuvant to enhance PRRSV GP5 immunogenicity. The Hsp70 gene was amplified by PCR from attenuated Mycobacterium bovis, and the PRRSV GP5 gene was amplified by RT-PCR from the total RNA of PRRSV SCQ strain which was isolated, identified and maintained by the Animal Biotechnological Center, Sichuan Agricultural University, China. The fusion expressing plasmid pET32-GP5-Hsp70 was constructed and expressed in Escherichia coli BL21. Ni2+-chelating resin was used to purify the His-tagged fusion protein expressed under optimized expressing conditions. The rabbit anti-GP5-Hsp70 fusion protein antibody was made, and Western blot assay verified the successful expression of the fusion protein, making it possible for further investigation whether Hsp70 could improve the immunogenicity of the PRRSV GP5 subunit vaccine, or evaluating the immunogenicity of the GP5-Hsp70 subunit vaccine.Keywords: Porcine reproductive and respiratory syndrome virus (PRRSV) GP5 gene, Mycobacterium bovis Hsp70 gene, cloning, prokaryotic expression, identification.African Journal of Biotechnology Vol. 12(30), pp. 4754-476

Onset of the Meissner effect at 65 K in FeSe thin film grown on Nb doped SrTiO3 substrate

We report the Meissner effect studies on an FeSe thin film grown on Nb doped SrTiO3 substrate by molecular beam epitaxy. Two-coil mutual inductance measurement clearly demonstrates the onset of diamagnetic screening at 65 K, which is consistent with the gap opening temperature determined by previous angle resolved photoemission spectroscopy results. The applied magnetic field causes a broadening of the superconducting transition near the onset temperature, which is the typical behavior for quasi-two-dimensional superconductors. Our results provide direct evidence that FeSe thin film grown on Nb doped SrTiO3 substrate has an onset TC ~ 65 K, which is the highest among all iron based superconductors discovered so far.Comment: 9 pages, 3 figures, updated reference lis

Puerarin inhibited oxidative stress and alleviated cerebral ischemia-reperfusion injury through PI3K/Akt/Nrf2 signaling pathway

Introduction: Puerarin (PUE) is a natural compound isolated from Puerariae Lobatae Radix, which has a neuroprotective effect on IS. We explored the therapeutic effect and underlying mechanism of PUE on cerebral I/R injury by inhibiting oxidative stress related to the PI3K/Akt/Nrf2 pathway in vitro and in vivo. Methods: The middle cerebral artery occlusion and reperfusion (MCAO/R) rats and oxygen-glucose deprivation and reperfusion (OGD/R) were selected as the models, respectively. The therapeutic effect of PUE was observed using triphenyl tetrazolium and hematoxylin-eosin staining. Tunel-NeuN staining and Nissl staining to quantify hippocampal apoptosis. The reactive oxygen species (ROS) level was detected by flow cytometry and immunofluorescence. Biochemical method to detect oxidative stress levels. The protein expression related to PI3K/ Akt/Nrf2 pathway was detected by using Western blotting. Finally, coimmunoprecipitation was used to study the molecular interaction between Keap1 and Nrf2. Results: In vivo and vitro studies showed that PUE improved neurological deficits in rats, as well as decreased oxidative stress. Immunofluorescence and flow cytometry indicated that the release of ROS can be inhibited by PUE. In addition, the Western blotting results showed that PUE promoted the phosphorylation of PI3K and Akt, and enabled Nrf2 to enter the nucleus, which further activated the expression of downstream antioxidant enzymes such as HO1. The combination of PUE with PI3K inhibitor LY294002 reversed these results. Finally, co-immunoprecipitation results showed that PUE promoted Nrf2-Keap1 complex dissociation. Discussion: Taken together, PUE can activate Nrf2 via PI3K/Akt and promote downstream antioxidant enzyme expression, which could further ameliorate oxidative stress, against I/R-induced Neuron injury

Targeting EZH2 Regulates Tumor Growth and Apoptosis Through Modulating Mitochondria Dependent Cell-Death Pathway in HNSCC

EZH2 is a negative prognostic factor and is overexpressed or activated in most human cancers including head and neck squamous cell carcinoma (HNSCC). Analysis of The Cancer Genome Atlas (TCGA) HNSCC data indicated that EZH2 over-expression was associated with high tumor grade and conferred poor prognosis. EZH2 inhibition triggered cell apoptosis, cell cycle arrest and decreased cell growth in vitro. MICU1 (mitochondrial calcium uptake1) was shown to be down regulated when EZH2 expression was inhibited in HNSCC. When the EZH2 and MICU1 were inhibited, HNSCC cells became susceptible to cell cycle arrest and apoptosis. Mitochondrial membrane potential and cytosolic Ca2+ concentration analysis suggested that EZH2 and MICU1 were required to maintain mitochondrial membrane potential stability. A xenograft tumor model was used to confirm that EZH2 depletion inhibited HNSCC cell growth and induced tumor cell apoptosis. In summary, EZH2 is a potential anti-tumor target in HNSCC