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Semiconducting ink based on two-dimensional (2D) single-crystal flakes with dangling-bond-free surfaces enables the implementation of high-performance devices on form-free substrates by cost-effective and scalable printing processes. However, the lack of solution-processed p-type 2D semiconducting inks with high mobility is an obstacle to the development of complementary integrated circuits. Here, we report a versatile strategy of doping with Br

We propose a deep learning-based framework for remote sensing image registration using equivariance features. Unlike conventional methods, the networks in the framework are trained on not invariance features but equivariance features since keeping invariancy in the areas of registration of remote sensing images can reduce the accuracy of matching results. Our framework is tested on four sets of KOMPSAT-3 remote sensing images and compared with the conventional machine learning based and the invariant feature based deep learning methods. The experimental results show that the proposed approach outperforms all the comparing methods.

In this paper, a convolutional neural network-based registration framework is proposed for remote sensing to improve the registration accuracy between two remote-sensed images acquired from different times and viewpoints. The proposed framework consists of four stages. In the first stage, key-points are extracted from two input images—a reference and a sensed image. Then, a patch is constructed at each key-point. The second stage consists of three processes for patch matching—candidate patch pair list generation, one-to-one matched label selection, and geometric distortion compensation. One-to-one matched patch pairs between two images are found, and the exact matching is found by compensating for geometric distortions in the matched patch pairs. A global geometric affine parameter set is computed using the random sample consensus algorithm (RANSAC) algorithm in the third stage. Finally, a registered image is generated after warping the input sensed image using the affine parameter set. The proposed high-accuracy registration framework is evaluated using the KOMPSAT-3 dataset by comparing the conventional frameworks based on machine learning and deep-learning-based frameworks. The proposed framework obtains the least root mean square error value of 34.922 based on all control points and achieves a 68.4% increase in the matching accuracy compared with the conventional registration framework.

Most flavonoids exist as sugar conjugates. Naturally occurring flavonoid sugar conjugates include glucose, galactose, glucuronide, rhamnose, xylose, and arabinose. These flavonoid glycosides have diverse physiological activities, depending on the type of sugar attached. To synthesize an unnatural flavonoid glycoside, Actinobacillus actinomycetemcomitans gene tll (encoding dTDP-6-deoxy- l -lyxo-4-hexulose reductase, which converts the endogenous nucleotide sugar dTDP-4-dehydro-6-deoxy- l -mannose to dTDP-6-deoxytalose) was introduced into Escherichia coli . In addition, nucleotide-sugar dependent glycosyltransferases (UGTs) were screened to find a UGT that could use dTDP-6-deoxytalose. Supplementation of this engineered strain of E. coli with quercetin resulted in the production of quercetin-3- O -(6-deoxytalose). To increase the production of quercetin 3- O -(6-deoxytalose) by increasing the supplement of dTDP-6-deoxytalose in E. coli , we engineered nucleotide biosynthetic genes of E. coli , such as galU (UTP-glucose 1-phosphate uridyltransferase), rffA (dTDP-4-oxo-6-deoxy- d -glucose transaminase), and/or rfbD (dTDP-4-dehydrorahmnose reductase). The engineered E. coli strain produced approximately 98 mg of quercetin 3- O -(6-deoxytalose)/liter, which is 7-fold more than that produced by the wild-type strain, and the by-products, quercetin 3- O -glucose and quercetin 3- O -rhamnose, were also significantly reduced.