Design, synthesis, and evaluation of fluorogenic cyanine dyes for nucleolar RNA imaging in living cells: effect of regioisomers on probe functions

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Design, synthesis, and evaluation of fluorogenic cyanine dyes for nucleolar RNA imaging in living cells: effect of regioisomers on probe functions

Tohoku University博士（理学）thesi

RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement

Extreme learning machine (ELM) as an emerging branch of shallow networks has shown its excellent generalization and fast learning speed. However, for blended data, the robustness of ELM is weak because its weights and biases of hidden nodes are set randomly. Moreover, the noisy data exert a negative effect. To solve this problem, a new framework called RMSE-ELM is proposed in this paper. It is a two-layer recursive model. In the first layer, the framework trains lots of ELMs in different groups concurrently, then employs selective ensemble to pick out an optimal set of ELMs in each group, which can be merged into a large group of ELMs called candidate pool. In the second layer, selective ensemble is recursively used on candidate pool to acquire the final ensemble. In the experiments, we apply UCI blended datasets to confirm the robustness of our new approach in two key aspects (mean square error and standard deviation). The space complexity of our method is increased to some degree, but the results have shown that RMSE-ELM significantly improves robustness with slightly computational time compared with representative methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.Comment: Accepted for publication in Mathematical Problems in Engineering, 09/22/201

Effect of L-Arginine or L-Lysine on the Quality of Duck Meat Patties during Freeze-thaw Cycles

In this study, the effects of L-arginine or L-lysine on the quality of duck meat patties during repeated freeze-thaw cycles were studied to provide a theoretical basis for the application of L-arginine or L-lysine as cryoprotectant in meat products. L-arginine or L-lysine was added in the marinating process of duck meat patties, and the prepared duck meat patties was treated with freeze-thaw cycles. The texture, cooking loss, color, pH, total volatile base nitrogen (TVB-N), thiobarbituric reactive substances (TBARS), low-field nuclear magnetic resonance, and microstructure were measured to evaluate the quality of duck meat patties. The results showed that with the increase of freeze-thaw cycles, the hardness, springiness, cohesiveness, chewiness, a* value, pH and P21 of duck meat patties in the blank group decreased significantly (P<0.05), while the cooking loss, TVB-N value and TBARS value increased significantly (P<0.05). After five freeze-thaw cycles, L-arginine or L-lysine significantly inhibited the deterioration of duck meat patties quality (P<0.05), and the cooking loss of duck meat patties in L-arginine group was 13.23% and 6.93% higher than those in blank group and sodium tripolyphosphate (STP) group, respectively (P<0.05). In addition, after five freeze-thaw cycles, the TVB-N value and TBARS value of L-arginine group were 41.92% and 63.47% lower than those of blank group (P<0.05), respectively, which were the lowest among the four groups. Therefore, the L-arginine or L-lysine treatment could effectively inhibit spoilage, the oxidation of fat, improve water retention, and maintain good quality characteristics of duck meat patties

Business groups and corporate social responsibility: Evidence from China

This study investigates the impact of firms' business group affiliations on their performance in corporate social responsibility (CSR) in the context of China. We find that firms with a dual-status of simultaneously being a business group member and a state-owned enterprise (SOE) have weaker CSR performance. Our finding is consistent with the view that CSR engagement is a strategy for firms to pursue political legitimacy from the government and seek legitimacy in general from the public. The business group affiliation and the SOE identity together afford legitimacy to the firm and reduce its need to conduct CSR activities

ThumbNet: One Thumbnail Image Contains All You Need for Recognition

Although deep convolutional neural networks (CNNs) have achieved great success in computer vision tasks, its real-world application is still impeded by its voracious demand of computational resources. Current works mostly seek to compress the network by reducing its parameters or parameter-incurred computation, neglecting the influence of the input image on the system complexity. Based on the fact that input images of a CNN contain substantial redundancy, in this paper, we propose a unified framework, dubbed as ThumbNet, to simultaneously accelerate and compress CNN models by enabling them to infer on one thumbnail image. We provide three effective strategies to train ThumbNet. In doing so, ThumbNet learns an inference network that performs equally well on small images as the original-input network on large images. With ThumbNet, not only do we obtain the thumbnail-input inference network that can drastically reduce computation and memory requirements, but also we obtain an image downscaler that can generate thumbnail images for generic classification tasks. Extensive experiments show the effectiveness of ThumbNet, and demonstrate that the thumbnail-input inference network learned by ThumbNet can adequately retain the accuracy of the original-input network even when the input images are downscaled 16 times

Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks

Solving image-to-3D from a single view is an ill-posed problem, and current neural reconstruction methods addressing it through diffusion models still rely on scene-specific optimization, constraining their generalization capability. To overcome the limitations of existing approaches regarding generalization and consistency, we introduce a novel neural rendering technique. Our approach employs the signed distance function as the surface representation and incorporates generalizable priors through geometry-encoding volumes and HyperNetworks. Specifically, our method builds neural encoding volumes from generated multi-view inputs. We adjust the weights of the SDF network conditioned on an input image at test-time to allow model adaptation to novel scenes in a feed-forward manner via HyperNetworks. To mitigate artifacts derived from the synthesized views, we propose the use of a volume transformer module to improve the aggregation of image features instead of processing each viewpoint separately. Through our proposed method, dubbed as Hyper-VolTran, we avoid the bottleneck of scene-specific optimization and maintain consistency across the images generated from multiple viewpoints. Our experiments show the advantages of our proposed approach with consistent results and rapid generation

LARSEN-ELM: Selective Ensemble of Extreme Learning Machines using LARS for Blended Data

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new machine learning framework called LARSEN-ELM for overcoming this problem. In our paper, we would like to show two key steps in LARSEN-ELM. In the first step, preprocessing, we select the input variables highly related to the output using least angle regression (LARS). In the second step, training, we employ Genetic Algorithm (GA) based selective ensemble and original ELM. In the experiments, we apply a sum of two sines and four datasets from UCI repository to verify the robustness of our approach. The experimental results show that compared with original ELM and other methods such as OP-ELM, GASEN-ELM and LSBoost, LARSEN-ELM significantly improve robustness performance while keeping a relatively high speed.Comment: Accepted for publication in Neurocomputing, 01/19/201

Up-regulation of MiR-205 under hypoxia promotes epithelial-mesenchymal transition by targeting ASPP2

The epithelial–mesenchymal transition (EMT) is one of the crucial procedures for cancer invasion and distal metastasis. Despite undergoing intensive studies, the mechanisms underlying EMT remain to be completely elucidated. Here, we identified that apoptosis-stimulating protein of p53-2 (ASPP2) is a novel target of MiR-205 in various cancers. Interestingly, the binding site of MiR-205 at the 3′-untranslated region of ASPP2 was highly conserved among different species. An inverse correlation between MiR-205 and ASPP2 was further observed in vivo in cervical cancers, suggesting MiR-205 may be an important physiological inhibitor of ASPP2. Hypoxia is a hallmark of solid tumor microenvironment and one of such conditions to induce EMT. Notably, MiR-205 was remarkably induced by hypoxia in cervical and lung cancer cells. A marked suppression of ASPP2 was observed simultaneously. Further studies confirmed that hypoxia-induced ASPP2 suppression was mainly attributed to the elevated MiR-205. Interestingly, the alteration of MiR-205/ASPP2 under hypoxia was accompanied with the decreased epithelial marker E-cadherin and increased mesenchymal marker Vimentin, as well as a morphological transition from the typical cobblestone-like appearance to the mesenchymal-like structure. More importantly, MiR-205 mimics or ASPP2 silencing similarly promoted EMT process. By contrast, ASPP2 recovery or MiR-205 inhibitor reversed MiR-205-dependent EMT. Further studies demonstrated that the newly revealed MiR-205/ASPP2 axis promoted cell migration and also increased cell proliferation both in vivo and in vitro. These data together implicated a critical impact of MiR-205/ASPP2 on promoting EMT. MiR-205/ASPP2 may be potential diagnostic and therapeutic biomarkers in cervical and lung cancers