The microRNA319 (miR319) family is conserved among diverse plant species. In rice (Oryza sativa L.), the miR319 gene family is comprised of two members, Osa-miR319a and Osa-miR319b. We found that ...overexpressing Osa-miR319b in rice resulted in wider leaf blades and delayed development. Here, we focused on the biological function and potential molecular mechanism of the Osa-miR319b gene in response to cold stress in rice. The expression of Osa-miR319b was down-regulated by cold stress, and the overexpression of Osa-miR319b led to an enhanced tolerance to cold stress, as evidenced by higher survival rates and proline content. Also, the expression of a handful of cold stress responsive genes, such as DREB1A/B/C, DREB2A, TPP1/2, was increased in Osa-miR319b transgenic lines. Furthermore, we demonstrated the nuclear localization of the transcription factors, OsPCF6 and OsTCP21, which may be Osa-miR319b-targeted genes. We also showed that OsPCF6 and OsTCP21 expression was largely induced by cold stress, and the degree of induction was obviously repressed in plants overexpressing Osa-miR319b. As expected, the down-regulation of OsPCF6 and OsTCP21 resulted in enhanced tolerance to cold stress, partially by modifying active oxygen scavenging. Taken together, our findings suggest that Osa-miR319b plays an important role in plant response to cold stress, maybe by targeting OsPCF6 and OsTCP21.
Binary descriptors have been widely used for efficient image matching and retrieval. However, most existing binary descriptors are designed with hand-craft sampling patterns or learned with label ...annotation provided by datasets. In this paper, we propose a new unsupervised deep learning approach, called DeepBit, to learn compact binary descriptor for efficient visual object matching. We enforce three criteria on binary descriptors which are learned at the top layer of the deep neural network: 1) minimal quantization loss, 2) evenly distributed codes and 3) transformation invariant bit. Then, we estimate the parameters of the network through the optimization of the proposed objectives with a back-propagation technique. Extensive experimental results on various visual recognition tasks demonstrate the effectiveness of the proposed approach. We further demonstrate our proposed approach can be realized on the simplified deep neural network, and enables efficient image matching and retrieval speed with very competitive accuracies.
Heat shock transcription factors (HSFs) play critical roles in several types of environmental stresses. However, the detailed regulatory mechanisms in response to salt stress are still largely ...unknown. In this study, we examined the salt-induced transcriptional responses of ThHSFA1-ThWRKY4 in Tamarix hispida and their functions and regulatory mechanisms in salt tolerance. ThHSFA1 protein acts as an upstream regulator that can directly activate ThWRKY4 expression by binding to the heat shock element (HSE) of the ThWRKY4 promoter using yeast one-hybrid (Y1H), chromatin immunoprecipitation (ChIP), and dual-luciferase reporter assays. ThHSFA1 and ThWRKY4 expression was significantly induced by salt stress and abscisic acid (ABA) treatment in the roots and leaves of T. hispida. ThHSFA1 is a nuclear-localized protein with transactivation activity at the C-terminus. Compared to nontransgenic plants, transgenic plants overexpressing ThHSFA1 displayed enhanced salt tolerance and exhibited reduced reactive oxygen species (ROS) levels and increased antioxidant enzyme activity levels under salt stress. Therefore, we further concluded that ThHSFA1 mediated the regulation of ThWRKY4 in response to salt stress in T. hispida.
Understanding kinetics including reaction pathways and associated transition rates is an important yet difficult problem in numerous chemical and biological systems, especially in situations with ...multiple competing pathways. When these high-dimensional systems are projected on low-dimensional coordinates, which are often needed for enhanced sampling or for interpretation of simulations and experiments, one can end up losing the kinetic connectivity of the underlying high-dimensional landscape. Thus, in the low-dimensional projection, metastable states might appear closer or further than they actually are. To deal with this issue, in this work, we develop a formalism that learns a multidimensional yet minimally complex reaction coordinate (RC) for generic high-dimensional systems. When projected along this RC, all possible kinetically relevant pathways can be demarcated and the true high-dimensional connectivity is maintained. One of the defining attributes of our method lies in that it can work on long unbiased simulations as well as biased simulations often needed for rare event systems. We demonstrate the utility of the method by studying a range of model systems including conformational transitions in a small peptide Ace-Ala3-Nme, where we show how two-dimensional and three-dimensional RCs found by our previously published spectral gap optimization method “SGOOP” Tiwary, P. and Berne, B. J. Proc. Natl. Acad. Sci. 2016, 113, 2839 can capture the kinetics for 23 and all 28 out of the 28 dominant state-to-state transitions, respectively.
Supervised deep learning with pixel-wise training labels has great successes on multi-person part segmentation. However, data labeling at pixel-level is very expensive. To solve the problem, people ...have been exploring to use synthetic data to avoid the data labeling. Although it is easy to generate labels for synthetic data, the results are much worse compared to those using real data and manual labeling. The degradation of the performance is mainly due to the domain gap, i.e., the discrepancy of the pixel value statistics between real and synthetic data. In this paper, we observe that real and synthetic humans both have a skeleton (pose) representation. We found that the skeletons can effectively bridge the synthetic and real domains during the training. Our proposed approach takes advantage of the rich and realistic variations of the real data and the easily obtainable labels of the synthetic data to learn multi-person part segmentation on real images without any human-annotated labels. Through experiments, we show that without any human labeling, our method performs comparably to several state-of-the-art approaches which require human labeling on Pascal-Person-Parts and COCO-DensePose datasets. On the other hand, if part labels are also available in the real-images during training, our method outperforms the supervised state-of-the-art methods by a large margin. We further demonstrate the generalizability of our method on predicting novel keypoints in real images where no real data labels are available for the novel keypoints detection. Code and pre-trained models are available at https://github.com/kevinlin311tw/CDCL-human-part-segmentation .
The present paper explores the impact of trade policy uncertainty (TPU) on agricultural commodity prices (ACP) by employing bootstrap full- and subsample rolling-window Granger causality tests. We ...find that TPU has both positive and negative effects on ACP, suggesting that TPU may change the supply of and demand for agricultural commodities, leading to fluctuations in ACP. These results support the hypotheses derived from the general equilibrium model, which highlights that TPU can significantly affect ACP. In turn, we find a positive impact of ACP on TPU, indicating that the agricultural commodity market reflects trade conditions in advance. In the context of Sino-U.S. trade frictions and the COVID-19 pandemic, the interaction between TPU and ACP can provide insights for governments to prevent large fluctuations in agricultural commodity markets and stabilize the national economy.
•We explore the role of trade policy uncertainty (TPU) in agricultural commodity prices (ACP).•We employed the bootstrap full- and sub-sample rolling-window Granger causality tests.•Our results shows both positive and negative effects of TPU to ACP.•ACP to TPU shows only positive impact.•Our results are supported by the interaction mechanism stating the mutual influence.
Summary of main observation and conclusion
A new class of indole‐based allylic donors have been designed and developed for palladium‐catalyzed decarboxylative allylations. In addition, the first ...application of these indole‐based allylic donors in palladium‐catalyzed decarboxylative 3+2 cycloaddition and allylic amination has been achieved by reacting with isocyanates and sulfonyl amines, respectively. This approach represents the first design of indole‐based allylic donors, which is helpful for settling the challenge of designing and developing new class of heterocycle‐based allylic donors for Pd‐catalyzed decarboxylative allylation reactions. Moreover, the application of this new class of allylic donors in cycloadditions and substitutions will add new contents to the research field of decarboxylative allylation.
A new class of indole‐based allylic donors have been designed and applied in palladium‐catalyzed decarboxylative 3+2 cycloaddition and allylic amination.
Depth-image-based rendering (DIBR) oriented view synthesis has been widely employed in the current depth-based 3-D video systems by synthesizing a virtual view from an arbitrary viewpoint. However, ...holes may appear in the synthesized view due to disocclusion, thus significantly degrading the quality. Consequently, efforts have been made on developing effective and efficient hole-filling algorithms. Current hole-filling techniques generally extrapolate/interpolate the hole regions with the neighboring information based on an assumption that the texture pattern in the holes is similar to that of the neighboring background information. However, in many scenarios, especially of complex texture, the assumption may not hold. In other words, hole-filling techniques can only provide an estimation for a hole which may not be good enough or may even be erroneous considering a wide variety of complex scene of images. In this paper, we first examine the view interpolation with multiple reference views, demonstrating that the problem of emerging holes in a target virtual view can be greatly alleviated by making good use of other neighboring complementary views in addition to its two (commonly used) most neighboring primary views. The effects of using multiple views for view extrapolation in reducing holes are also investigated in this paper. In view of the 3D Video and ongoing free-viewpoint TV standardization, we propose a new view synthesis framework, which employs multiple views to synthesize output virtual views. Furthermore, a scheme of selective warping of complementary views is developed by efficiently locating a small number of useful pixels in the complementary views for hole reduction, to avoid full warping of additional complementary views thus lowering greatly the warping complexity. Experimental results show that the hole size based on two primary reference views may be reduced by up to about 70% with the help of two complementary reference views in the case of view interpolation, while the hole size based on one primary reference view may be reduced by about 27% with the help of one more complementary reference view in view extrapolation. Moreover, it is shown that by using one more pair of views in view interpolation and one more view in view extrapolation, 10% hole pixels may be reduced additionally.
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